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Pipeline run

01d626cb-24d3-4f05-9fd9-65aea91e9799

Pipeline LLM cost (USD)
API 1: $0.0040 API 2: $0.1728 API 3: $0.0000 Total: $0.1768

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work
no_db_connection
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): Machine Learning
Evidence — skills matched in JD (17)
C C++ x86 ARM SSE AVX NEON AV1 H.265 Git Machine Learning Neural Networks H.264 MPEG-2 VP9 OpenCL CUDA
Skill cluster (0 dimension groups, role-scoped)
No dimension groups computed for this JD.
Show KRA description ↓
The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology. The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to): • Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs • Work involves implementation of high quality video encoders, decoders and transcoders and associated intellectual properties like Motion estimation, Rate Control algorithms, Scene Cut Detection, Fade-in / Fade-out Compensation, De-interlacing, De-noising as an example • Working on latest technology of Machine learning and Neural Network based video compression • Knowledge of C/C++ • Knowledge of x86 based development, intrinsic like SSE, AVX based coding • Knowledge of ARM based development, intrinsic like Neon coding • Debugging, profiling and development environments • Good knowledge of video standards like AV1 and H.265 • Working knowledge of H.264, MPEG-2 and VP9 is good to possess • Software Processes, Git, Configuration Management, Test Planning and Execution • Exposure to multi-threaded, cache optimal designs of video codecs • Exposure to OpenCL based GPU development / CUDA based programming • Aware of Machine learning and Neural Network basics.

Signals

Skill ml-engineer
0.12
Alias ar-vr-engineer
0.59
KRA ml-engineer
0.31

Post-classification

Centroid
Alias collision log
New-role queue#43
New skills captured0
New KRA captured
Status: extract_details_done Created: 2026-05-20T11:21:04.276999Z Updated: 2026-05-20T11:33:23.760099Z
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Machine Learning Engineer

CASE E

slug: machine-learning-engineer · id: — · source: llm

The role aligns with the primary skills of C, C++, Machine Learning, and Neural Networks.

Job description

Role : Video Codec Engineer 

Required Experience: Candidates must have development experience ranging from 2 to 4 years.
• Experience in implementing video compression standards based and/or proprietary Image and Video codecs/algorithms
• Must have exposure and development experience ARM and/or x86 based platforms like Xeon E5/E3, Core-i7/i5
• Experience of development using operating systems like Windows / Linux / OS X

Job Description: The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology.

Responsibility: The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to):
• Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs
• Work involves implementation of high quality video encoders, decoders and transcoders and associated intellectual properties like Motion estimation, Rate Control algorithms, Scene Cut Detection, Fade-in / Fade-out Compensation, De-interlacing, De-noising as an example
• Working on latest technology of Machine learning and Neural Network based video compression

Educational Qualification: Masters or Bachelor’s Degree in Computer Science / Electronics and Communication

Required Technical Skills:
• Knowledge of C/C++
• Knowledge of x86 based development, intrinsic like SSE, AVX based coding
• Knowledge of ARM based development, intrinsic like Neon coding
• Debugging, profiling and development environments
• Good knowledge of video standards like AV1 and H.265
• Working knowledge of H.264, MPEG-2 and VP9 is good to possess
• Software Processes, Git, Configuration Management, Test Planning and Execution
• Exposure to multi-threaded, cache optimal designs of video codecs
• Exposure to OpenCL based GPU development / CUDA based programming
• Aware of Machine learning and Neural Network basics.

Location: Bengaluru, Karnataka

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

C Primary Library skill Existing skill (matched library)
Canonical: C# id=4 · c

Aliases — catalog

  • C# (CANONICAL) primary
  • C (CANONICAL)
  • C# 1 (VERSION)
  • C# 10 (VERSION)
  • C# 11 (VERSION)
  • C# 12 (VERSION)
  • C# 13 (VERSION)
  • C# 14 (VERSION)
  • C# 2 (VERSION)
  • C# 3 (VERSION)
  • C# 4 (VERSION)
  • C# 5 (VERSION)
  • C# 6 (VERSION)
  • C# 7 (VERSION)
  • C# 8 (VERSION)
  • C# 9 (VERSION)
  • C# latest (VERSION)
  • C#1 (VERSION)
  • C#10 (VERSION)
  • C#11 (VERSION)
  • C#12 (VERSION)
  • C#2 (VERSION)
  • C#3 (VERSION)
  • C#4 (VERSION)
  • C#5 (VERSION)
  • C#6 (VERSION)
  • C#7 (VERSION)
  • C#8 (VERSION)
  • C#9 (VERSION)
  • C++ (CANONICAL)
  • C++03 (VERSION)
  • C++11 (VERSION)
  • C++14 (VERSION)
  • C++17 (VERSION)
  • C++20 (VERSION)
  • C++23 (VERSION)
  • C++26 (VERSION)
  • C++98 (VERSION)
  • c sharp (VERSION)
  • c# (VERSION)
  • cpp03 (VERSION)
  • cpp11 (VERSION)
  • cpp14 (VERSION)
  • cpp17 (VERSION)
  • cpp20 (VERSION)
  • cpp23 (VERSION)
  • cpp26 (VERSION)
  • cpp98 (VERSION)
  • csharp (VERSION)
  • modern C++ (VERSION)

Context tags (catalog)

.NET ASP.NET Azure Boost C# 9 C11 C99 CLR CMake Entity Framework LINQ MSBuild MVC Makefile NuGet Qt RAII Roslyn STL TDD Unity Visual Studio WPF Web API WinForms Xamarin async/await attributes buffer overflow constexpr delegates dependency injection dynamic memory events free function pointer gcc gdb header files inline interfaces linker malloc memory leak memory management move semantics multithreading operator overloading pointer pointers preprocessor segmentation fault smart pointers stdio.h stdlib.h struct syntax error templates typedef xUnit

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Microsoft
License
mit
Year introduced
2000
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
latest

Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cross-Platform App Languages Catalog dimension db id 167

    Library dimension (catalog)

    Roles linked in library: Hybrid Mobile Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Full Stack Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

C++ Primary Library skill Existing skill (matched library)
Canonical: C# id=4 · c

Aliases — catalog

  • C# (CANONICAL) primary
  • C (CANONICAL)
  • C# 1 (VERSION)
  • C# 10 (VERSION)
  • C# 11 (VERSION)
  • C# 12 (VERSION)
  • C# 13 (VERSION)
  • C# 14 (VERSION)
  • C# 2 (VERSION)
  • C# 3 (VERSION)
  • C# 4 (VERSION)
  • C# 5 (VERSION)
  • C# 6 (VERSION)
  • C# 7 (VERSION)
  • C# 8 (VERSION)
  • C# 9 (VERSION)
  • C# latest (VERSION)
  • C#1 (VERSION)
  • C#10 (VERSION)
  • C#11 (VERSION)
  • C#12 (VERSION)
  • C#2 (VERSION)
  • C#3 (VERSION)
  • C#4 (VERSION)
  • C#5 (VERSION)
  • C#6 (VERSION)
  • C#7 (VERSION)
  • C#8 (VERSION)
  • C#9 (VERSION)
  • C++ (CANONICAL)
  • C++03 (VERSION)
  • C++11 (VERSION)
  • C++14 (VERSION)
  • C++17 (VERSION)
  • C++20 (VERSION)
  • C++23 (VERSION)
  • C++26 (VERSION)
  • C++98 (VERSION)
  • c sharp (VERSION)
  • c# (VERSION)
  • cpp03 (VERSION)
  • cpp11 (VERSION)
  • cpp14 (VERSION)
  • cpp17 (VERSION)
  • cpp20 (VERSION)
  • cpp23 (VERSION)
  • cpp26 (VERSION)
  • cpp98 (VERSION)
  • csharp (VERSION)
  • modern C++ (VERSION)

Context tags (catalog)

.NET ASP.NET Azure Boost C# 9 C11 C99 CLR CMake Entity Framework LINQ MSBuild MVC Makefile NuGet Qt RAII Roslyn STL TDD Unity Visual Studio WPF Web API WinForms Xamarin async/await attributes buffer overflow constexpr delegates dependency injection dynamic memory events free function pointer gcc gdb header files inline interfaces linker malloc memory leak memory management move semantics multithreading operator overloading pointer pointers preprocessor segmentation fault smart pointers stdio.h stdlib.h struct syntax error templates typedef xUnit

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Microsoft
License
mit
Year introduced
2000
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
latest

Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cross-Platform App Languages Catalog dimension db id 167

    Library dimension (catalog)

    Roles linked in library: Hybrid Mobile Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Full Stack Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

x86 Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.96

x86 remains a core ISA in server, desktop, and systems JDs; Intel/AMD roadmaps and widespread Linux/Windows support show sustained market demand, not replacement.

Vendor & license

(0.95)

Context keywords
x86_64 IA-32 assembly_language Intel AMD microarchitecture opcode registers virtual_memory instruction_pipeline x86_emulation 64-bit 32-bit system_call memory_management
Ambiguity low

“x86” is a specific CPU instruction set architecture term; unlikely to be confused with other catalog skills.

Versioning

Not versioned

Type assignment

Concept ·instruction_set_architecture confidence 0.93

x86 is fundamentally an instruction set architecture, which fits the Architecture vs Concept rule as a system-shape/technical knowledge unit rather than a language, tool, or runtime.

Derived legacy fields
Category
Concept
Sub-category
instruction_set_architecture
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • x86 Architecture and Assembly

    Pipeline tentative id

    Low-level programming and platform knowledge for the x86 instruction set and processor architecture. This belongs here because x86 is the core CPU ISA used for assembly, ABI-level work, and performance-sensitive systems code.

ARM Primary Library skill Existing skill (matched library)
Canonical: ARM id=1621 · arm

Aliases — catalog

  • AArch64 (VERSION)
  • ARM (CANONICAL)
  • ARMv8 (VERSION)
  • arm64 (VERSION)

Context tags (catalog)

ARM Cortex ARMv7 ARMv8 JTAG NEON RISC SoC Thumb assembly language cross-compilation debugging embedded systems hardware abstraction low-level programming microcontroller real-time operating systems

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Instruction Set Architecture
Confidence
0.91
Version strategy
SEPARATE_ENTITY
Version tag
ARMv8-A

Maturity reasoning: ARM is a dominant instruction-set architecture in mobile, embedded, and increasingly server/cloud chips; job postings commonly mention ARM64/AArch64 alongside Linux and systems work.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1222
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

SSE Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.78

SSE appears in more job descriptions for real-time web features, but it is still far less common than WebSockets; GitHub usage and framework docs have grown, yet it remains a secondary protocol choice.

Vendor & license

(0.90)

Context keywords
EventSource streaming real-time HTTP push notifications client-server browser support JSON CORS long polling data format reconnection SSE fallback server-side asynchronous web applications
Ambiguity low

SSE is a specific web streaming protocol (Server-Sent Events); “SSE” in JDs typically maps to that, not another catalog skill.

Versioning

Not versioned

Type assignment

Protocol ·server_sent_events confidence 0.93

SSE (Server-Sent Events) is a communication standard for one-way event streaming over HTTP, so by the Protocol rule it is a protocol rather than a format or architecture.

Derived legacy fields
Category
Protocol
Sub-category
server_sent_events
Skill nature
PROTOCOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Server-Sent Events

    Pipeline tentative id

    Browser-to-server streaming over HTTP using the SSE protocol. This belongs here because SSE is a specific mechanism for pushing incremental updates, notifications, and live data to clients.

AVX Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity niche confidence 0.86

AVX appears in low-volume hardware/low-level systems JDs, usually as a bonus for SIMD optimization rather than a core requirement; market demand is far narrower than mainstream software stacks.

Vendor & license

Intel ·unknown ·since 2011 (0.85)

Context keywords
SIMD vectorization performance optimization floating-point assembly language instruction pipelining cache alignment multithreading compiler flags memory bandwidth parallel processing microarchitecture register allocation low-level programming hardware acceleration
Ambiguity low

AVX is a specific CPU instruction set extension; unlikely to be confused with other catalog skills in typical JDs.

Versioning

Not versioned

Type assignment

Standard ·instruction_set_extension_standard confidence 0.90

AVX is a named CPU instruction-set specification, so by the Standard rule it is an industry-defined specification rather than a language, tool, or concept.

Derived legacy fields
Category
Standard
Sub-category
instruction_set_extension_standard
Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • SIMD Instruction Set Extensions

    Pipeline tentative id

    Low-level CPU vector instruction extensions used to accelerate data-parallel computation. AVX belongs here because it is an x86 SIMD extension used for high-throughput arithmetic and media processing.

NEON Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.84

NEON appears in cloud database JDs and vendor docs as a Postgres serverless option, but JD volume is still far below AWS RDS/PostgreSQL and it’s not yet a universal hiring staple.

Vendor & license

Neon Labs, Inc. ·apache_2 ·since 2021 (0.90)

Context keywords
data modeling real-time analytics cloud integration data pipeline SQL compatibility data visualization scalability data warehousing ETL processes API access multi-tenancy data governance performance tuning security protocols user management
Ambiguity low

“NEON” most commonly refers to the NEON Postgres platform; it’s not a common acronym that overlaps with other catalog skills.

Versioning

Not versioned

Type assignment

Platform ·database_platform confidence 0.90

By the Vendor SaaS = Platform rule, NEON is the hosted multi-tenant Neon database service rather than software you run yourself, so it fits Platform.

Derived legacy fields
Category
Platform
Sub-category
database_platform
Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • SIMD and Vector Intrinsics

    Pipeline tentative id

    Low-level vectorized programming primitives used to accelerate compute-heavy code paths with data-parallel operations. NEON belongs here because it is ARM's SIMD instruction set and intrinsic API for writing optimized media and signal-processing code.

  • Video Codec Optimization

    Pipeline tentative id

    Implementation and optimization techniques for efficient video encoding and decoding pipelines. NEON fits here when used to accelerate codec hot paths such as motion compensation, transforms, filtering, and pixel processing.

AV1 Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.84

AV1 is increasingly requested in streaming/media JDs and supported by major vendors (YouTube, Netflix, Chrome/Firefox), but it is still far less universal than H.264/H.265 in job postings.

Vendor & license

Alliance for Open Media ·other_open ·since 2018 (0.95)

Context keywords
video compression codec bitrate streaming encoding decoding container format HEVC H.264 video quality adaptive streaming low latency AV1 profile media player transcoding
Ambiguity low

AV1 is a specific video codec standard name; unlikely to be confused with other catalog skills.

Versioning

Not versioned

Type assignment

Standard ·video_codec_standard confidence 0.93

AV1 is fundamentally an industry-defined specification for video compression, so by the Standard rule it fits Standard rather than Format or Protocol.

Derived legacy fields
Category
Standard
Sub-category
video_codec_standard
Skill nature
STANDARD
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Codec Standards

    Pipeline tentative id

    Standards and specifications for video compression formats used to encode, decode, and transport video efficiently. AV1 belongs here because it is a modern open video codec standard with defined bitstream, profiles, and interoperability requirements.

  • Video Codec Implementation

    Pipeline tentative id

    Implementation of video codecs in software or firmware, including encoder and decoder behavior, optimization, and conformance testing. AV1 fits here when the skill refers to building or integrating codec logic rather than only understanding the standard.

H.265 Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.84

H.265/HEVC is widely used in streaming and device pipelines; job postings for video/codec roles still mention HEVC alongside H.264, and major vendors ship hardware decode support.

Vendor & license

ITU-T ·unknown ·since 2013 (0.85)

Context keywords
HEVC video compression bitrate streaming encoding decoding video quality inter-frame intra-frame profile level container format hardware acceleration codec transcoding
Ambiguity low

H.265 is a specific video codec identifier; JDs typically won’t confuse it with other distinct codec formats.

Versioning

Not versioned

Type assignment

Format ·video_codec_format confidence 0.90

H.265 (HEVC) is a video compression specification that defines how media is encoded/structured, so it fits the Format category rather than a Protocol or Concept.

Derived legacy fields
Category
Format
Sub-category
video_codec_format
Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Codec Standards

    Pipeline tentative id

    Standards and specifications for compressing and decoding digital video streams. H.265 belongs here because it is a video compression codec standard used for efficient encoding, transmission, and playback.

H.264 Secondary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

H.264 remains a standard codec in job postings for video streaming, conferencing, and media pipelines; it is widely supported by browsers, devices, and vendor docs, with no announced sunset or replacement in general use.

Vendor & license

ITU-T ·other_open ·since 2003 (0.90)

Context keywords
AVC video compression bitrate profile level CABAC B-frames I-frames P-frames MP4 streaming decoding encoding video quality interframe prediction
Ambiguity flagged

Could be confused with: h-265

H.264 is commonly mentioned alongside H.265/HEVC in video codec contexts; JDs may refer to either without clear distinction.

Versioning

Not versioned

Type assignment

Format ·video_codec_format confidence 0.90

H.264 is a video compression specification/bitstream format rather than software or a communication protocol, so it fits the Format type.

Derived legacy fields
Category
Format
Sub-category
video_codec_format
Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Codec Standards

    Pipeline tentative id

    Standards and specifications for compressing and decoding digital video streams. H.264 belongs here because it is a widely used video compression codec defined by the AVC standard.

MPEG-2 Secondary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity deprecated confidence 0.93

MPEG-2 is largely superseded in new video workflows by H.264/H.265 and AV1; recent job postings rarely list it except for legacy broadcast/DVD support, and modern vendor docs position newer codecs as the default.

Vendor & license

Moving Picture Experts Group ·unknown ·since 1994 (0.90)

Context keywords
video encoding bitrate compression artifacts streaming MPEG-2 Transport Stream MPEG-2 Program Stream video quality decoding muxing demuxing interlaced video frame rate video standards broadcasting digital video
Ambiguity flagged

Could be confused with: mpeg-4, h-264

MPEG-2 in JDs is often mentioned alongside other video codecs/formats like MPEG-4 or H.264, and extractors may map it to the wrong codec.

Versioning

Not versioned

Type assignment

Format ·video_compression_format confidence 0.93

MPEG-2 is an industry-defined media compression specification, so by the Format vs Standard rule it is best treated as a data/wire format rather than a protocol or concept.

Derived legacy fields
Category
Format
Sub-category
video_compression_format
Skill nature
STANDARD
Volatility
DEPRECATED
Typical lifespan
SHORT_LIVED
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Compression Standards

    Pipeline tentative id

    Standards and specifications for encoding and decoding digital video streams. MPEG-2 belongs here because it is a foundational video compression format used to represent, transport, and decode compressed video content.

  • Digital Video Container Formats

    Pipeline tentative id

    Packaging and multiplexing formats used to store or transport compressed media streams. MPEG-2 is often encountered as part of MPEG-2 Program Stream or Transport Stream workflows, so it can sit in this packaging dimension as well.

VP9 Secondary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity niche confidence 0.86

VP9 appears in some media/codec JDs and browser/video stack work, but market demand is far lower than H.264/AV1 and it is not a common hiring-pipeline staple.

Vendor & license

(0.95)

Context keywords
video codec compression streaming WebM bitrate quality encoding decoding AV1 H.264 media container adaptive streaming video quality low latency video conferencing
Ambiguity low

VP9 is a specific video codec standard; typical JDs won’t confuse it with other unrelated skills in the catalog.

Versioning

Not versioned

Type assignment

Concept ·general confidence 0.00

Stage 4 failed; fallback typed record.

Derived legacy fields
Category
Concept
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Codec Standards

    Pipeline tentative id

    Standards and bitstream formats for compressed video encoding and decoding. VP9 belongs here because it is a specific video codec specification used to represent and transmit video efficiently.

  • Video Codec Implementation

    Pipeline tentative id

    Engineering of encoder and decoder software for video compression formats. VP9 fits here when the skill is about implementing, optimizing, or integrating the codec in a media stack rather than just knowing the standard.

Git Primary Library skill Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Version Control Tool
Vendor
Linus Torvalds
License
gpl_v2
Year introduced
2005
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
730
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

OpenCL Secondary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity niche confidence 0.91

OpenCL appears in far fewer job postings than CUDA or Vulkan compute, and most new GPU compute roles specify CUDA/ROCm instead; market demand is concentrated in specialized cross-vendor HPC/embedded work.

Vendor & license

Khronos Group ·other_open ·since 2008 (0.90)

Context keywords
GPU parallel processing kernel compute device clCreateBuffer clEnqueueNDRangeKernel OpenCL C platform context memory management clFinish clGetDeviceInfo workgroup event interoperability
Ambiguity low

OpenCL is a specific parallel computing standard; unlikely to be confused with other catalog skills in typical JDs.

Versioning

Versioned 3.0

{
  "OpenCL 3": "3.0",
  "OpenCL 3.0": "3.0"
}
Type assignment

Standard ·parallel_computing_standard confidence 0.90

OpenCL is an industry specification for heterogeneous parallel computing, so by the Standard rule it is a standard rather than a language or framework.

Derived legacy fields
Category
Standard
Sub-category
parallel_computing_standard
Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • GPU Parallel Programming

    Pipeline tentative id

    Programming models and APIs for writing compute workloads that run on GPUs and other accelerators. OpenCL belongs here because it defines kernels, memory transfers, and execution control for heterogeneous parallel computation.

CUDA Secondary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

CUDA appears in many ML/HPC job postings and is the standard NVIDIA GPU programming stack; it remains the default target for GPU acceleration rather than a sunset technology.

Vendor & license

NVIDIA ·proprietary ·since 2006 (0.95)

Context keywords
GPU parallel computing CUDA cores NVIDIA cuDNN TensorRT thrust OpenCL kernel memory management CUDA Toolkit device memory streaming CUDA-aware profiling synchronous execution
Ambiguity low

CUDA is a specific NVIDIA GPU programming platform; typical JDs won’t confuse it with other catalog skills.

Versioning

Versioned 12.4

{
  "CUDA 12": "12.4",
  "CUDA 12.4": "12.4",
  "CUDA 12.x": "12.4",
  "cuda12": "12.4",
  "cuda12.4": "12.4"
}
Type assignment

Language ·gpu_programming_language confidence 0.90

CUDA is fundamentally a programming language/API model for writing GPU code, so it fits the Language type rather than a tool or framework.

Derived legacy fields
Category
Language
Sub-category
gpu_programming_language
Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • GPU Parallel Programming

    Pipeline tentative id

    Programming models and APIs for writing high-performance code that runs on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing GPU kernels, memory transfers, and device-side execution.

Machine Learning Primary Library skill Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, ML Ops Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Neural Networks Primary New / orchestrated New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.96

Broadly listed in ML/AI job descriptions and core to modern deep learning stacks; major frameworks like PyTorch and TensorFlow center on neural networks.

Vendor & license

(0.95)

Context keywords
TensorFlow Keras PyTorch backpropagation activation functions convolutional layers recurrent networks dropout gradient descent hyperparameters transfer learning training data overfitting model evaluation neuron
Ambiguity low

“Neural Networks” is a specific ML model concept; typical JDs won’t confuse it with other distinct skills in the catalog.

Versioning

Not versioned

Type assignment

Concept ·machine_learning_model_concept confidence 0.96

Neural Networks are a named knowledge unit about model structure and learning, so by the Concept vs Methodology rule they are a Concept rather than an Architecture or Framework.

Derived legacy fields
Category
Concept
Sub-category
machine_learning_model_concept
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

Locked dimensions (v3 placement)

  • Neural Network Frameworks

    Reuses catalog slug

    Core libraries and APIs used to define, train, and run neural network models. Neural Networks belongs here because the skill is typically expressed through model-building frameworks rather than standalone infrastructure or governance topics.

  • ML Frameworks and Libraries

    Reuses catalog slug

    Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.

Library artifacts (this run)

No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleVideo Codec Engineer
ExperienceCandidates must have development experience ranging from 2 to 4 years.
DomainOther
Location Bengaluru, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science / Electronics and Communication",
      "raw": "Masters or Bachelor\u2019s Degree in Computer Science / Electronics and Communication",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 4,
    "min": 2,
    "raw": "Candidates must have development experience ranging from 2 to 4 years."
  },
  "job_locations": [
    {
      "aliases": [
        "Bangalore"
      ],
      "city": "Bengaluru",
      "country": "India",
      "state": "Karnataka",
      "work_mode": null
    }
  ],
  "role": "Video Codec Engineer",
  "role_aliases": [
    "Codec Engineer",
    "Video Engineer",
    "Video Compression Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Job Description",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "The prospective candidate will be",
        "last_5_words": "in video technology."
      },
      "text": "The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology.",
      "word_count": 27
    },
    {
      "bullet_count": 3,
      "heading": "Responsibility",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "The key responsibilities of the",
        "last_5_words": "video compression"
      },
      "text": "The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to):\n\u2022 Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs\n\u2022 Work involves implementation of high quality video encoders, decoders and transcoders and associated intellectual properties like Motion estimation, Rate Control algorithms, Scene Cut Detection, Fade-in / Fade-out Compensation, De-interlacing, De-noising as an example\n\u2022 Working on latest technology of Machine learning and Neural Network based video compression",
      "word_count": 64
    },
    {
      "bullet_count": 10,
      "heading": "Required Technical Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Knowledge of C/C++",
        "last_5_words": "and Neural Network basics."
      },
      "text": "\u2022 Knowledge of C/C++\n\u2022 Knowledge of x86 based development, intrinsic like SSE, AVX based coding\n\u2022 Knowledge of ARM based development, intrinsic like Neon coding\n\u2022 Debugging, profiling and development environments\n\u2022 Good knowledge of video standards like AV1 and H.265\n\u2022 Working knowledge of H.264, MPEG-2 and VP9 is good to possess\n\u2022 Software Processes, Git, Configuration Management, Test Planning and Execution\n\u2022 Exposure to multi-threaded, cache optimal designs of video codecs\n\u2022 Exposure to OpenCL based GPU development / CUDA based programming\n\u2022 Aware of Machine learning and Neural Network basics.",
      "word_count": 104
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "C"
    },
    {
      "is_primary": true,
      "skill_name": "C++"
    },
    {
      "is_primary": true,
      "skill_name": "x86"
    },
    {
      "is_primary": true,
      "skill_name": "ARM"
    },
    {
      "is_primary": true,
      "skill_name": "SSE"
    },
    {
      "is_primary": true,
      "skill_name": "AVX"
    },
    {
      "is_primary": true,
      "skill_name": "NEON"
    },
    {
      "is_primary": true,
      "skill_name": "AV1"
    },
    {
      "is_primary": true,
      "skill_name": "H.265"
    },
    {
      "is_primary": false,
      "skill_name": "H.264"
    },
    {
      "is_primary": false,
      "skill_name": "MPEG-2"
    },
    {
      "is_primary": false,
      "skill_name": "VP9"
    },
    {
      "is_primary": true,
      "skill_name": "Git"
    },
    {
      "is_primary": false,
      "skill_name": "OpenCL"
    },
    {
      "is_primary": false,
      "skill_name": "CUDA"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Neural Networks"
    }
  ],
  "jd_role": {
    "display_name": "Video Codec Engineer",
    "rationale": null,
    "role_aliases": [
      "Codec Engineer",
      "Video Engineer",
      "Video Compression Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
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      },
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    },
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        "raw": "Masters or Bachelor\u2019s Degree in Computer Science / Electronics and Communication",
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      }
    ],
    "experience": {
      "max": 4,
      "min": 2,
      "raw": "Candidates must have development experience ranging from 2 to 4 years."
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        "work_mode": null
      }
    ],
    "role": "Video Codec Engineer",
    "role_aliases": [
      "Codec Engineer",
      "Video Engineer",
      "Video Compression Engineer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
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        "bullet_count": 0,
        "heading": "Job Description",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "The prospective candidate will be",
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        },
        "text": "The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology.",
        "word_count": 27
      },
      {
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        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "The key responsibilities of the",
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        },
        "text": "The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to):\n\u2022 Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs\n\u2022 Work involves implementation of high quality video encoders, decoders and transcoders and associated intellectual properties like Motion estimation, Rate Control algorithms, Scene Cut Detection, Fade-in / Fade-out Compensation, De-interlacing, De-noising as an example\n\u2022 Working on latest technology of Machine learning and Neural Network based video compression",
        "word_count": 64
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      {
        "bullet_count": 10,
        "heading": "Required Technical Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Knowledge of C/C++",
          "last_5_words": "and Neural Network basics."
        },
        "text": "\u2022 Knowledge of C/C++\n\u2022 Knowledge of x86 based development, intrinsic like SSE, AVX based coding\n\u2022 Knowledge of ARM based development, intrinsic like Neon coding\n\u2022 Debugging, profiling and development environments\n\u2022 Good knowledge of video standards like AV1 and H.265\n\u2022 Working knowledge of H.264, MPEG-2 and VP9 is good to possess\n\u2022 Software Processes, Git, Configuration Management, Test Planning and Execution\n\u2022 Exposure to multi-threaded, cache optimal designs of video codecs\n\u2022 Exposure to OpenCL based GPU development / CUDA based programming\n\u2022 Aware of Machine learning and Neural Network basics.",
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    "urls": []
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  "rejected": false,
  "rejection_reason": null,
  "run_id": "01d626cb-24d3-4f05-9fd9-65aea91e9799",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
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        "matched_count": null,
        "role_id": 8,
        "score": 0.5882,
        "slug": "ar-vr-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.5,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "matched_count": null,
        "role_id": 13,
        "score": 0.5,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "Frontend Engineer",
        "matched_count": null,
        "role_id": 7,
        "score": 0.5,
        "slug": "frontend-engineer",
        "total_count": null
      },
      {
        "display_name": "Full Stack Engineer",
        "matched_count": null,
        "role_id": 15,
        "score": 0.5,
        "slug": "full-stack-engineer",
        "total_count": null
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    ],
    "kra_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.314,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "matched_count": null,
        "role_id": 10,
        "score": 0.3086,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Data Engineer",
        "matched_count": null,
        "role_id": 2,
        "score": 0.3079,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Hybrid Mobile Developer",
        "matched_count": null,
        "role_id": 11,
        "score": 0.2872,
        "slug": "hybrid-mobile-developer",
        "total_count": null
      },
      {
        "display_name": "Full Stack Engineer",
        "matched_count": null,
        "role_id": 15,
        "score": 0.2785,
        "slug": "full-stack-engineer",
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    ],
    "skill_match_roles": [
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        "display_name": "ML Engineer",
        "matched_count": 2,
        "role_id": 3,
        "score": 0.1176,
        "slug": "ml-engineer",
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      {
        "display_name": "ML Ops Engineer",
        "matched_count": 2,
        "role_id": 16,
        "score": 0.1176,
        "slug": "ml-ops-engineer",
        "total_count": 17
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": 1,
        "role_id": 1,
        "score": 0.0588,
        "slug": "backend-engineer",
        "total_count": 17
      },
      {
        "display_name": "AI Engineer",
        "matched_count": 1,
        "role_id": 13,
        "score": 0.0588,
        "slug": "ai-engineer",
        "total_count": 17
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      {
        "display_name": "Full Stack Engineer",
        "matched_count": 1,
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    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "E",
    "chosen_role": null,
    "confidence": 0.0,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "queued": true,
    "reasoning": "low_kra: top KRA 0.31 \u003c 0.4"
  },
  "stage5_updates": {
    "centroid_n_after": null,
    "centroid_updated": false,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [],
    "queue_entry_id": 43,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1609,
      "existing_alias_text": "C",
      "input_term": "C",
      "matched_canonical": {
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        "display_name": "C#",
        "id": 4,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "c",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 418,
      "existing_alias_text": "C++",
      "input_term": "C++",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "C#",
        "id": 4,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "c",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2573,
      "existing_alias_text": "ARM",
      "input_term": "ARM",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "ARM",
        "id": 1621,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "arm",
        "sub_category_id": 1222,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1613,
      "existing_alias_text": "Git",
      "input_term": "Git",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Git",
        "id": 1002,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 730,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2015,
      "existing_alias_text": "Machine Learning",
      "input_term": "Machine Learning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Hybrid Mobile Developer",
      "id": 11,
      "rationale": null,
      "role_archetype": null,
      "slug": "hybrid-mobile-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Engineer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
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      "display_name": "Full Stack Engineer",
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      "role_archetype": null,
      "slug": "full-stack-engineer",
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    },
    {
      "display_name": "ML Engineer",
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      "rationale": null,
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      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "ML Ops Engineer",
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      "rationale": null,
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      "slug": "ml-ops-engineer",
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    },
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      "display_name": "AR/VR Engineer",
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      "rationale": null,
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      "slug": "ar-vr-engineer",
      "source": "db"
    },
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      "display_name": "AI Engineer",
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      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Machine Learning Engineer",
    "id": null,
    "rationale": "The role aligns with the primary skills of C, C++, Machine Learning, and Neural Networks.",
    "role_archetype": "An engineer focused on developing machine learning applications and algorithms using programming languages like C and C++.",
    "slug": "machine-learning-engineer",
    "source": "llm"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cross-Platform App Languages",
        "id": 167,
        "rationale": "Languages used to implement shared mobile features across iOS and Android from a common codebase. This is the primary coding surface for hybrid app logic, UI behavior, and platform-specific branching.",
        "slug": "cross-platform-app-languages",
        "source": "db"
      },
      "input_skill": "C",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Hybrid Mobile Developer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "hybrid-mobile-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
        "slug": "programming-languages",
        "source": "db"
      },
      "input_skill": "C",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
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        "merge_log": [],
        "placed": {
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          "requires": [],
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          "suppress_on_match": []
        },
        "skill_id": "opencl",
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        "warnings": []
      },
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    },
    {
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          },
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          "roles_from_db": []
        }
      ],
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        },
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          },
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          "skill_id": "cuda",
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              "cuda12.4": "12.4"
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            "versioned": true
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Programming models and APIs for writing high-performance code that runs on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing GPU kernels, memory transfers, and device-side execution.",
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              "CUDA C++",
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            "out_of_scope": "Kubernetes GPU scheduling, container runtime setup, cloud GPU instance selection, shader authoring, CPU-only optimization, general ML framework usage",
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              {
                "reason": "GPU-enabled scheduling and deployment can involve CUDA-adjacent systems work, but this dimension is about the programming model rather than orchestration.",
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                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Some ML libraries expose CUDA-backed acceleration, but CUDA itself is the lower-level GPU programming layer.",
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                "with_dim_name": null,
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            ],
            "tentative_id": "d_init_01"
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        ],
        "merge_log": [],
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        "relationships": {
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          "parent_skills": [],
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          "requires": [],
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          "suppress_on_match": []
        },
        "skill_id": "cuda",
        "split_log": [],
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    {
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      ],
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          ]
        },
        {
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      ],
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    {
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          },
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          "roles_from_db": [
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        },
        {
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          },
          "input_skill": "Neural Networks",
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          ]
        }
      ],
      "input_skill": "Neural Networks",
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      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
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        "enrichment": {
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          "context_keywords": {
            "context_keywords": [
              "TensorFlow",
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          "skill_id": "neural-networks",
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        },
        "keep_log": [
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            "a_role": "__skill_focal__",
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        ],
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            "out_of_scope": "workflow orchestration, experiment tracking, deployment platforms, model governance, data labeling, GPU cluster management",
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              {
                "reason": "Neural network training often uses the same tools, but this dimension is about model definition and execution rather than run comparison or metrics management.",
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                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
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            ],
            "tentative_id": "ml-frameworks-and-libraries"
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          {
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            "name": "ML Frameworks and Libraries",
            "out_of_scope": "Adjacent clusters explicitly not owned by ML Frameworks and Libraries, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "ml-frameworks-and-libraries"
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        ],
        "merge_log": [],
        "placed": {
          "name": "Neural Networks",
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          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
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        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "tensorflow",
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            "scikit-learn",
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            "multimodal-document-understanding",
            "anomaly-detection",
            "agentic-systems",
            "ai-infrastructure"
          ],
          "requires": [],
          "skill_id": "neural-networks",
          "suppress_on_match": []
        },
        "skill_id": "neural-networks",
        "split_log": [],
        "typed": {
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          "name": "Neural Networks",
          "reasoning": "Neural Networks are a named knowledge unit about model structure and learning, so by the Concept vs Methodology rule they are a Concept rather than an Architecture or Framework.",
          "skill_id": "neural-networks",
          "subtype": "machine_learning_model_concept",
          "type": "Concept"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "x86",
    "SSE",
    "AVX",
    "NEON",
    "AV1",
    "H.265",
    "H.264",
    "MPEG-2",
    "VP9",
    "OpenCL",
    "CUDA",
    "Neural Networks"
  ]
}
API 3 — final-role-output
{}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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