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Pipeline run

e79f432c-cf5c-4ad5-848c-ebe534d2304b

Pipeline LLM cost (USD)
API 1: $0.0040 API 2: $0.1606 API 3: $0.0000 Total: $0.1646

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 video-codec-engineer
1.00
KRA ml-engineer
0.31
Status: extract_details_done Created: 2026-05-20T14:27:53.881017Z Updated: 2026-05-20T14:41:15.156812Z
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

Video Codec Engineer

slug: video-codec-engineer · id: — · source: llm

The primary skills indicate a strong focus on video codec development using languages like C and C++ along with machine learning techniques.

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 0.95

x86 remains a dominant ISA in server/desktop JDs and vendor roadmaps; Intel and AMD continue active platform support, with widespread Linux/Windows deployment and toolchain compatibility.

Vendor & license

(0.90)

Context keywords
Intel AMD x64 assembly microarchitecture instruction set 64-bit 32-bit virtualization BIOS machine code registers pipeline cache coherence hyper-threading
Ambiguity low

“x86” specifically denotes the x86 instruction set architecture; it’s unlikely to be confused with other distinct ISA/architecture skills in typical JDs.

Versioning

Not versioned

Type assignment

Architecture ·instruction_set_architecture confidence 0.97

x86 is fundamentally an instruction set architecture, so it fits the Architecture type rather than a language, tool, or runtime.

Derived legacy fields
Category
Architecture
Sub-category
instruction_set_architecture
Skill nature
PATTERN
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)

  • x86 Architecture and Assembly

    Pipeline tentative id

    Low-level x86 CPU architecture knowledge used to write, read, and optimize machine code. This fits the target skill because x86 is the instruction-set and execution model underlying assembly-level performance work, binary compatibility, and codec hot paths.

  • SIMD and CPU Intrinsics

    Pipeline tentative id

    Vectorized CPU programming techniques for accelerating compute-heavy workloads on x86 processors. This belongs here when x86 is used as the target platform for SSE, AVX, and intrinsic-based optimization in media and codec pipelines.

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)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

No v3 new_skill_meta for this skill (orchestrator skipped or failed).

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 systems/embedded/HPC job postings and compiler/CPU optimization docs, but is rarely a standalone hiring requirement versus broader SIMD/C++ performance skills.

Vendor & license

Intel ·since 2011 (0.90)

Context keywords
SIMD vectorization performance optimization floating-point assembly language microarchitecture registers instruction throughput cache alignment parallel processing compiler optimization low-level programming hardware acceleration memory bandwidth CPU architecture
Ambiguity low

AVX is a specific CPU instruction set extension; typical JDs won’t confuse it with other unrelated skills.

Versioning

Not versioned

Type assignment

Standard ·instruction_set_extension_standard confidence 0.90

AVX is an industry-defined CPU instruction set specification, so by the Standard rule it is a standard 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 Vector Instruction Sets

    Pipeline tentative id

    Low-level CPU instruction set extensions used to process multiple data elements in parallel. AVX belongs here because it is an x86 SIMD extension used for performance-critical media, numeric, and signal-processing code.

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

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.78

NEON appears increasingly in cloud database JDs and vendor docs as a serverless Postgres platform, but it is far from universal compared with AWS RDS/PostgreSQL; market signal is growing job-listing adoption rather than broad staple status.

Vendor & license

Neon Labs ·apache_2 ·since 2021 (0.90)

Context keywords
data ingestion real-time analytics cloud-native data pipeline SQL NoSQL data modeling event-driven scalability data visualization API integration multi-tenant data governance stream processing data lake
Ambiguity low

“NEON” as a database platform is specific; typical JDs won’t confuse it with other common skills in the catalog.

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)

Locked dimensions (v3 placement)

  • SIMD and Vector Intrinsics

    Pipeline tentative id

    Low-level CPU vectorization APIs and instruction-set intrinsics used to accelerate media, signal-processing, and numerical code. NEON belongs here because it is ARM's SIMD extension for writing data-parallel operations directly against the processor.

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 H.264/H.265 still dominate many production pipelines.

Vendor & license

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

Context keywords
video streaming codec efficiency bitrate compression video quality decoding encoding media container interoperability HDR 4K streaming protocols AV1 decoder AV1 encoder video playback open source
Ambiguity flagged

Could be confused with: h264, hevc

AV1 is a video codec acronym; JDs may mention codecs generically or confuse it with other common codecs like H.264/HEVC.

Versioning

Not versioned

Type assignment

Format ·video_codec_format confidence 0.90

AV1 is fundamentally a video compression specification/bitstream format rather than a software product, so it fits the Format type.

Derived legacy fields
Category
Format
Sub-category
video_codec_format
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 compressed video formats used in encoding, decoding, and streaming pipelines. AV1 belongs here because it is a specific modern video codec standard rather than a general media tool.

  • Video Encoding and Transcoding

    Pipeline tentative id

    Implementation of media processing pipelines that encode, decode, transcode, and optimize video assets for delivery. AV1 belongs here when the skill is used in practical encoder integration, pipeline tuning, or format conversion work.

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

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

No v3 new_skill_meta for this skill (orchestrator skipped or failed).

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

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.92

H.264 remains a default video codec in job specs for streaming, conferencing, and media pipelines, and is still broadly supported by major vendors and browsers; market demand is sustained despite newer codecs.

Vendor & license

ITU-T ·unknown ·since 2003 (0.85)

Context keywords
AVC video compression bitrate codec MP4 streaming decoding encoding profile level inter-frame intra-frame B-frames P-frames CABAC NAL units
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, so by the Format rule it is a data/wire representation rather than a software system or protocol.

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; current job postings rarely list it except for legacy broadcast/DVD systems.

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 decoding video quality interlaced video frame rate video codec multimedia digital broadcasting video editing content delivery
Ambiguity flagged

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

MPEG-2 is a video compression standard and JDs may mention generic MPEG/codec terms that could be extracted as other common codecs/formats.

Versioning

Versioned 2

{
  "MPEG 2": "2",
  "MPEG-2": "2",
  "MPEG2": "2"
}
Type assignment

Format ·video_compression_format confidence 0.90

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

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

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Video Compression Standards

    Pipeline tentative id

    Standards and codecs used to compress, encode, and decode digital video for storage, broadcast, and streaming. MPEG-2 belongs here because it is a foundational video compression format with defined bitstream syntax and interoperability requirements.

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/streaming JDs and browser/video tooling, but job-posting volume is far below H.264/AV1 and it is not a common hiring-pipeline staple.

Vendor & license

Google ·bsd ·since 2013 (0.95)

Context keywords
WebM AV1 video streaming video compression bitrate codec container format media player adaptive streaming video quality encoding decoding lossy compression streaming protocol video playback
Ambiguity low

“VP9” is a specific video codec format name; unlikely to be confused with other catalog skills.

Versioning

Not versioned

Type assignment

Format ·video_codec_format confidence 0.90

VP9 is fundamentally a video compression specification/bitstream format rather than a language, tool, or datastore, 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

    Compression standards and bitstream formats used to encode and decode digital video. VP9 belongs here because it is a specific video codec standard with defined syntax, profiles, and interoperability requirements.

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)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

No v3 new_skill_meta for this skill (orchestrator skipped or failed).

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; vendor docs and ecosystem tooling (cuDNN, TensorRT) reinforce broad adoption.

Vendor & license

NVIDIA ·proprietary ·since 2006 (0.95)

Context keywords
GPU parallel computing CUDA cores NVIDIA cuDNN TensorRT OpenCL kernel memory management thrust CUDA Toolkit streaming performance optimization device query CUDA-aware MPI
Ambiguity low

CUDA is a specific NVIDIA GPU programming platform/language; typical JDs use it unambiguously versus other skills.

Versioning

Versioned 12.x

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

Language ·gpu_programming_language confidence 0.90

CUDA is fundamentally a programming language/toolchain for writing GPU kernels and host-device code, so it fits the Language type rather than a library 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 compute kernels that run efficiently on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing parallel execution, memory movement, and device-side optimization.

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

Neural networks are a core ML concept widely listed in job descriptions for ML/AI roles and underpin mainstream frameworks like PyTorch and TensorFlow, indicating broad market adoption.

Vendor & license

(0.95)

Context keywords
TensorFlow Keras PyTorch backpropagation activation functions convolutional layers recurrent networks dropout gradient descent overfitting transfer learning hyperparameters model training loss function 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 0.97

Neural Networks are a named knowledge unit in machine 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 as a standalone infrastructure concern.

  • 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
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API 1 — extract-from-jd click to toggle
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API 2 — extract-details
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              "MPEG-2",
              "MPEG-1",
              "H.262",
              "video encoding",
              "video decoding",
              "bitstream syntax",
              "GOP structure"
            ],
            "in_scope": "MPEG-2, MPEG-1, H.262, video bitstream syntax, GOP structure, I-frames P-frames B-frames, quantization, entropy coding, profile and level constraints, encoder/decoder interoperability",
            "name": "Video Compression Standards",
            "out_of_scope": "Container formats and muxing such as MP4, MKV, TS, audio codecs such as AAC and MP3, streaming delivery protocols such as HLS and DASH, GPU video acceleration APIs",
            "overlap_flags": [
              {
                "reason": "Video packaging and multiplexing can be adjacent, but this dimension is about the compression standard itself rather than artifact assembly.",
                "with_dim_id": "build-and-packaging-tooling",
                "with_dim_name": null,
                "with_role": "DevOps Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "MPEG-2",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "mpeg-2"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "webrtc",
            "bandwidth-adaptation",
            "rendering-efficiency",
            "recording",
            "dvc",
            "metrics"
          ],
          "requires": [],
          "skill_id": "mpeg-2",
          "suppress_on_match": []
        },
        "skill_id": "mpeg-2",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "MPEG-2",
          "reasoning": "MPEG-2 is an industry-defined media encoding specification, so by the Format vs Standard rule it is best treated as a format rather than a tool or concept.",
          "skill_id": "mpeg-2",
          "subtype": "video_compression_format",
          "type": "Format"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "VP9",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "VP9",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Format",
          "skill_nature": "STANDARD",
          "sub_category": "video_codec_format",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cVP9\u201d is a specific video codec format name; unlikely to be confused with other catalog skills."
          },
          "context_keywords": {
            "context_keywords": [
              "WebM",
              "AV1",
              "video streaming",
              "video compression",
              "bitrate",
              "codec",
              "container format",
              "media player",
              "adaptive streaming",
              "video quality",
              "encoding",
              "decoding",
              "lossy compression",
              "streaming protocol",
              "video playback"
            ]
          },
          "maturity": {
            "confidence": 0.86,
            "maturity": "niche",
            "reasoning": "VP9 appears in some media/streaming JDs and browser/video tooling, but job-posting volume is far below H.264/AV1 and it is not a common hiring-pipeline staple."
          },
          "skill_id": "vp9",
          "vendor_license": {
            "confidence": 0.95,
            "license": "bsd",
            "vendor": "Google",
            "year_introduced": 2013
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Compression standards and bitstream formats used to encode and decode digital video. VP9 belongs here because it is a specific video codec standard with defined syntax, profiles, and interoperability requirements.",
            "exemplar_skills": [
              "VP9",
              "AV1",
              "H.264",
              "H.265",
              "MPEG-2 Video",
              "video bitstream parsing",
              "rate control"
            ],
            "in_scope": "VP9, AV1, H.264/AVC, H.265/HEVC, MPEG-2 Video, bitstream syntax, profiles and levels, intra/inter prediction, entropy coding, rate control, decoder compatibility",
            "name": "Video Codec Standards",
            "out_of_scope": "Container formats like MP4 or WebM, streaming protocols like HLS or DASH, GPU rendering pipelines, audio codecs, which belong to separate media or transport dimensions",
            "overlap_flags": [
              {
                "reason": "Video codecs are often implemented alongside media playback and streaming delivery stacks, so there can be overlap with playback or transport-oriented dimensions.",
                "with_dim_id": "d_init_02",
                "with_dim_name": null,
                "with_role": null
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "VP9",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "vp9"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "webrtc",
            "dvc",
            "api",
            "gcp",
            "ocr",
            "wireguard",
            "ios"
          ],
          "requires": [],
          "skill_id": "vp9",
          "suppress_on_match": []
        },
        "skill_id": "vp9",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "VP9",
          "reasoning": "VP9 is fundamentally a video compression specification/bitstream format rather than a language, tool, or datastore, so it fits the Format type.",
          "skill_id": "vp9",
          "subtype": "video_codec_format",
          "type": "Format"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Git",
          "alias_type": "CANONICAL",
          "id": 1613,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "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"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Git",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Git",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "OpenCL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "CUDA",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "CUDA",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Language",
          "skill_nature": "LANGUAGE",
          "sub_category": "gpu_programming_language",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "SEPARATE_ENTITY",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "CUDA is a specific NVIDIA GPU programming platform/language; typical JDs use it unambiguously versus other skills."
          },
          "context_keywords": {
            "context_keywords": [
              "GPU",
              "parallel computing",
              "CUDA cores",
              "NVIDIA",
              "cuDNN",
              "TensorRT",
              "OpenCL",
              "kernel",
              "memory management",
              "thrust",
              "CUDA Toolkit",
              "streaming",
              "performance optimization",
              "device query",
              "CUDA-aware MPI"
            ]
          },
          "maturity": {
            "confidence": 0.93,
            "maturity": "well_known",
            "reasoning": "CUDA appears in many ML/HPC job postings and is the standard NVIDIA GPU programming stack; vendor docs and ecosystem tooling (cuDNN, TensorRT) reinforce broad adoption."
          },
          "skill_id": "cuda",
          "vendor_license": {
            "confidence": 0.95,
            "license": "proprietary",
            "vendor": "NVIDIA",
            "year_introduced": 2006
          },
          "versioning": {
            "current_version": "12.x",
            "version_aliases": {
              "CUDA 12": "12.x",
              "CUDA 12.x": "12.x",
              "cuda 12": "12.x",
              "cuda12": "12.x"
            },
            "versioned": true
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Programming models and APIs for writing compute kernels that run efficiently on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing parallel execution, memory movement, and device-side optimization.",
            "exemplar_skills": [
              "CUDA",
              "CUDA C++",
              "GPU kernels",
              "shared memory optimization",
              "warp-level programming",
              "stream synchronization"
            ],
            "in_scope": "CUDA, CUDA C/C++, kernels, thread blocks, warps, shared memory, global memory, streams, events, device synchronization, occupancy tuning, memory coalescing",
            "name": "GPU Parallel Programming",
            "out_of_scope": "GPU deployment on Kubernetes, container scheduling, and cluster autoscaling, video codec algorithm design, CPU-only SIMD optimization, OpenCL or Vulkan compute programming",
            "overlap_flags": [
              {
                "reason": "CUDA work often includes low-level performance tuning, but this dimension is specifically about GPU programming rather than general application optimization.",
                "with_dim_id": "performance-and-stability-tuning",
                "with_dim_name": null,
                "with_role": "Android Engineer, Ios engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "CUDA",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "cuda"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "python",
            "tensorflow",
            "pytorch",
            "pytorch-ddp",
            "nvidia-gpu-operator",
            "rendering-efficiency",
            "arm",
            "api"
          ],
          "requires": [],
          "skill_id": "cuda",
          "suppress_on_match": []
        },
        "skill_id": "cuda",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "CUDA",
          "reasoning": "CUDA is fundamentally a programming language/toolchain for writing GPU kernels and host-device code, so it fits the Language type rather than a library or framework.",
          "skill_id": "cuda",
          "subtype": "gpu_programming_language",
          "type": "Language"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 2015,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "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"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "AI Governance and Model Security",
            "id": 50,
            "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
            "slug": "ai-governance-and-model-security",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
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            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "ML Frameworks and Libraries",
            "id": 40,
            "rationale": "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.",
            "slug": "ml-frameworks-and-libraries",
            "source": "db"
          },
          "input_skill": "Neural Networks",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "ML Frameworks and Libraries",
            "id": 40,
            "rationale": "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.",
            "slug": "ml-frameworks-and-libraries",
            "source": "db"
          },
          "input_skill": "Neural Networks",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Neural Networks",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "machine_learning_model_concept",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cNeural Networks\u201d is a specific ML model concept; typical JDs won\u2019t confuse it with other distinct skills in the catalog."
          },
          "context_keywords": {
            "context_keywords": [
              "TensorFlow",
              "Keras",
              "PyTorch",
              "backpropagation",
              "activation functions",
              "convolutional layers",
              "recurrent networks",
              "dropout",
              "gradient descent",
              "overfitting",
              "transfer learning",
              "hyperparameters",
              "model training",
              "loss function",
              "neuron"
            ]
          },
          "maturity": {
            "confidence": 0.96,
            "maturity": "well_known",
            "reasoning": "Neural networks are a core ML concept widely listed in job descriptions for ML/AI roles and underpin mainstream frameworks like PyTorch and TensorFlow, indicating broad market adoption."
          },
          "skill_id": "neural-networks",
          "vendor_license": {
            "confidence": 0.95,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "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 as a standalone infrastructure concern.",
            "exemplar_skills": [
              "Neural Networks",
              "TensorFlow",
              "PyTorch",
              "Keras",
              "JAX",
              "model training",
              "inference"
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            "in_scope": "Neural Networks, TensorFlow, PyTorch, Keras, JAX, model definition, backpropagation implementation, training loops, inference APIs",
            "name": "Neural Network Frameworks",
            "out_of_scope": "Workflow scheduling and orchestration of training jobs, experiment comparison and metric tracking, model governance and safety controls, deployment of models as services",
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              {
                "reason": "Neural network work often includes training metrics and validation, but that dimension owns run comparison and evaluation workflows.",
                "with_dim_id": "experiment-tracking-and-evaluation",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Neural networks may be deployed for inference, but serving infrastructure is owned by the deployment dimension.",
                "with_dim_id": "llm-serving-deployment",
                "with_dim_name": null,
                "with_role": "AI Engineer"
              }
            ],
            "tentative_id": "ml-frameworks-and-libraries"
          },
          {
            "description": "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.",
            "exemplar_skills": [
              "ML Frameworks and Libraries"
            ],
            "in_scope": "Skills, tools, and practices that belong under ML Frameworks and Libraries for the target role, including items implied by the dimension rationale.",
            "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"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Neural Networks",
          "placement_confidence": 0.92,
          "primary_dimension": "ml-frameworks-and-libraries",
          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "neural-networks"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "scikit-learn",
            "tensorflow",
            "pytorch",
            "vision-language-models",
            "multimodal-document-understanding",
            "anomaly-detection",
            "agentic-systems",
            "hybrid-retrieval"
          ],
          "requires": [],
          "skill_id": "neural-networks",
          "suppress_on_match": []
        },
        "skill_id": "neural-networks",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.97,
          "name": "Neural Networks",
          "reasoning": "Neural Networks are a named knowledge unit in machine 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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