Pipeline run

43111a7b-8610-40f9-8389-4923edda8b07

Pipeline LLM cost (USD)

API 1: $0.0037 API 2: $0.0735 API 3: $0.0000 Total: $0.0772

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

Nature of work · Video Codec Software Engineering

Build and optimize video codec software: implement encoders/decoders, transcoding and pre/post-processing algorithms for x86 and ARM, then debug, profile, and tune them for standards like AV1 and H.265.

"Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs"

Tech stack maturity

Mainstream Legacy cache hit

The stack centers on low-level C with SIMD optimization on ARM/NEON and SSE, which is typical of established performance-critical codec engineering rather than cloud-native or bleeding-edge AI tooling.

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

0.50 / 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 SSE AVX ARM NEON Git AV1 H.265 H.264 MPEG-2 VP9 OpenCL CUDA Machine Learning Neural Networks

Skill cluster (5 dimension groups, role-scoped)

Codec Standards and Bitstreams

AV1 VP9

Hardware Acceleration and SIMD

SSE NEON

Video Codec Languages and DSLs

C C++

AI Governance and Model Security

Machine Learning

Cross-cutting / unaligned

x86 AVX ARM Git H.265 H.264 MPEG-2 OpenCL CUDA Neural Networks

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 video-codec-engineer

0.29

Alias video-codec-engineer

1.00

KRA video-codec-engineer

0.52

Post-classification

Centroidupdated · n=2

Alias collision log—

New-role queue—

New skills captured8

New KRA captured—

Captured for admin review

x86 primary ↔ Video Codec Engineer pending

AVX primary ↔ Video Codec Engineer pending

H.265 primary ↔ Video Codec Engineer pending

H.264 ↔ Video Codec Engineer pending

MPEG-2 ↔ Video Codec Engineer pending

OpenCL ↔ Video Codec Engineer pending

CUDA ↔ Video Codec Engineer pending

Neural Networks ↔ Video Codec Engineer pending

Status: extract_details_done Created: 2026-05-20T14:39:43.521738Z Updated: 2026-05-20T14:55:21.634329Z

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

CASE A

slug: video-codec-engineer · id: 22 · source: db

The primary skills focus heavily on video codec languages and hardware acceleration, essential for a Video Codec Engineer.

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)
Video Codec Languages and DSLs Catalog dimension db id 225

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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)

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)
Video Codec Languages and DSLs Catalog dimension db id 225

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.97

x86 remains a core ISA in server, desktop, and systems JDs; Intel/AMD roadmaps and widespread compiler/toolchain support show no sunset or replacement in mainstream hiring.

Vendor & license

(0.95)

Context keywords

IA-32 x64 Intel AMD assembly language microarchitecture registers instruction pipeline virtual memory system calls x86-64 machine code performance optimization compatibility legacy support

Ambiguity low

“x86” is a specific, well-known CPU instruction set architecture; unlikely to be confused with other distinct ISA concepts in typical JDs.

Versioning

Not versioned

Type assignment

Concept ·instruction_set_architecture confidence 0.90

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 Instruction Set Architecture
Pipeline tentative id

Covers the x86 CPU architecture, its instruction set, registers, addressing modes, and execution model. This skill belongs here because x86 is the core platform knowledge used when writing, optimizing, or debugging low-level codec code on Intel/AMD systems.

SSE Primary Library skill Existing skill (matched library)

Canonical: SSE id=1718 · sse

Aliases — catalog

SSE (CANONICAL) primary

Context tags (catalog)

CPU architecture SIMD assembly language cache optimization compiler optimizations data-level parallelism hardware acceleration intrinsics low-level programming memory alignment multi-threading parallel processing performance optimization register allocation vectorization

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Simd Instruction Set Extension
Confidence: 0.98
Version strategy: NOT_APPLICABLE

Maturity reasoning: SSE is a long-established x86 SIMD extension; it appears in systems/performance JDs and is widely supported by compilers and CPUs, though newer AVX/AVX2/AVX-512 often supersede it for greenfield optimization.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 1277
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Hardware Acceleration and SIMD Catalog dimension db id 235

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

AVX Primary Library skill Existing skill (matched library)

Canonical: AVX2 id=1716 · avx2

Aliases — catalog

AVX2 (CANONICAL) primary

Context tags (catalog)

AVX-512 FMA SIMD bit manipulation cache utilization compiler optimizations data-level parallelism floating-point operations instruction pipelining intrinsics microarchitecture multithreading parallel processing performance optimization vectorization

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Simd Instruction Set Extension
Confidence: 0.98
Version strategy: NOT_APPLICABLE

Maturity reasoning: AVX2 appears in specialized systems/performance JDs, but far less often than mainstream platforms; it’s a CPU SIMD extension used in HPC, media, and low-level optimization rather than a broad hiring staple.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 1277
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Hardware Acceleration and SIMD Catalog dimension db id 235

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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)

NEON Primary Library skill Existing skill (matched library)

Canonical: NEON id=1717 · neon

Aliases — catalog

NEON (CANONICAL) primary

Context tags (catalog)

SIMD algorithm optimization bit manipulation compiler support data parallelism embedded systems floating-point operations hardware acceleration instruction set low-level programming memory alignment multithreading parallel processing performance optimization vectorization

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Simd Instruction Set Extension
Confidence: 0.98
Version strategy: NOT_APPLICABLE

Maturity reasoning: ARM NEON is a standard SIMD extension on mobile/embedded ARM chips and appears in many performance/embedded JDs and compiler docs, especially for multimedia and ML acceleration.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 1277
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Hardware Acceleration and SIMD Catalog dimension db id 235

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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)

AV1 Primary Library skill Existing skill (matched library)

Canonical: AV1 id=1678 · av1

Aliases — catalog

AV1 (CANONICAL) primary

Context tags (catalog)

AV1 decoder AV1 encoder HEVC adaptive streaming bitrate codec container formats decoding encoding interoperability media playback streaming video compression video delivery video quality

Stored enrichment (catalog DB)

Category: Standard
Sub-category: Video Codec Standard
Vendor: Alliance for Open Media
License: other_open
Year introduced: 2018
Confidence: 0.96
Version strategy: NOT_APPLICABLE

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

Skill profile (library / DB)

Skill nature: STANDARD
Volatility: EMERGING
Typical lifespan: EVERGREEN
Category id: 12
Sub-category id: 1308
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Codec Standards and Bitstreams Catalog dimension db id 227

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

H.265 Primary Library skill Existing skill (matched library)

Canonical: H.265/HEVC id=1677 · h-265-hevc

Aliases — catalog

H.265/HEVC (CANONICAL) primary

Context tags (catalog)

4K HDR bitrate codec container format decoding encoding hardware acceleration inter-frame intra-frame level profile streaming video compression video quality

Stored enrichment (catalog DB)

Category: Standard
Sub-category: Video Codec Standard
Vendor: ITU-T
License: unknown
Year introduced: 2013
Confidence: 0.97
Version strategy: NOT_APPLICABLE

Maturity reasoning: Widely used in streaming, broadcast, and device pipelines; job ads for video/codec engineers still mention HEVC alongside H.264/AV1, and major vendors ship hardware decode/encode support.

Skill profile (library / DB)

Skill nature: STANDARD
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 12
Sub-category id: 1308
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Codec Standards and Bitstreams Catalog dimension db id 227

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

H.264 Secondary Library skill Existing skill (matched library)

Canonical: H.264/AVC id=1676 · h-264-avc

Aliases — catalog

H.264/AVC (CANONICAL) primary

Context tags (catalog)

AVC B-frame CABAC HLS I-frame MP4 P-frame bitrate container format entropy coding level profile streaming video compression video encoding

Stored enrichment (catalog DB)

Category: Standard
Sub-category: Video Codec Standard
Vendor: ITU-T
License: unknown
Year introduced: 2003
Confidence: 0.97
Version strategy: NOT_APPLICABLE

Maturity reasoning: H.264/AVC is still widely required in video streaming, conferencing, and hardware encoding/decoding JDs; it remains a default codec in major vendor stacks despite newer alternatives like AV1.

Skill profile (library / DB)

Skill nature: STANDARD
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 12
Sub-category id: 1308
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Codec Standards and Bitstreams Catalog dimension db id 227

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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 deployments by H.264/H.265 and AV1; recent job postings rarely list it except for legacy broadcast/DVD systems, indicating low current market demand.

Vendor & license

ISO/IEC ·unknown ·since 1994 (0.85)

Context keywords

video encoding bitrate compression artifacts streaming MPEG-2 Transport Stream video quality decoding interlaced video frame rate digital broadcasting multimedia video codec container format video playback broadcast standards

Ambiguity flagged

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

MPEG-2 is often mentioned alongside other video compression standards (e.g., MPEG-4, H.264), and JDs may use generic phrasing that could map to different codecs.

Versioning

Not versioned

Type assignment

Standard ·video_compression_standard confidence 0.96

MPEG-2 is an industry-defined specification for digital video/audio compression and transmission, so it fits the Standard category rather than a Format or Protocol.

Derived legacy fields

Category: Standard
Sub-category: video_compression_standard
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 compressing and encoding digital video for storage, broadcast, and transmission. MPEG-2 belongs here as a foundational video codec standard used in broadcast and disc media workflows.
Digital Video Encoding
Pipeline tentative id

Practical encoding concepts and workflows used to produce compressed video streams for playback and distribution. MPEG-2 fits because it is a core encoding format used in broadcast, DVD, and transport-stream pipelines.

VP9 Secondary Library skill Existing skill (matched library)

Canonical: VP9 id=1679 · vp9

Aliases — catalog

VP9 (CANONICAL) primary

Context tags (catalog)

AV1 WebM adaptive streaming bitrate codec optimization decoding encoding hardware acceleration lossless compression media container real-time streaming video compression video conferencing video quality video streaming

Stored enrichment (catalog DB)

Category: Standard
Sub-category: Video Codec Standard
Vendor: Google
License: bsd
Year introduced: 2013
Confidence: 0.95
Version strategy: NOT_APPLICABLE

Maturity reasoning: VP9 appears in some media/streaming and browser JDs, but far less often than H.264/AV1; market demand is limited and it’s largely overshadowed by AV1 in new deployments.

Skill profile (library / DB)

Skill nature: STANDARD
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 12
Sub-category id: 1308
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Codec Standards and Bitstreams Catalog dimension db id 227

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

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

Skill enrichment (orchestrator / LLM)

Maturity niche confidence 0.88

OpenCL still appears in GPU/HPC job postings, but far less often than CUDA or vendor SDKs; Khronos continues maintenance, yet market demand is specialized rather than broad.

Vendor & license

Khronos Group ·other_open ·since 2008 (0.95)

Context keywords

GPU parallel processing kernel device memory model compute units OpenCL C platforms clCreateBuffer clEnqueueNDRangeKernel clFinish clGetDeviceInfo interoperability profiling task parallelism

Ambiguity low

OpenCL is a specific parallel computing framework/language; typical JDs won’t confuse it with other catalog skills.

Versioning

Not versioned

Type assignment

Language ·parallel_computing_language confidence 0.90

OpenCL is best treated as a programming language/API specification for expressing parallel kernels and host code, so it fits the Language type rather than a tool or framework.

Derived legacy fields

Category: Language
Sub-category: parallel_computing_language
Skill nature: LANGUAGE
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)

Parallel GPU Computing
Pipeline tentative id

Programming models and APIs for writing parallel compute kernels that run on GPUs and other accelerators. OpenCL belongs here because it defines portable kernel execution, memory management, and host-device coordination for heterogeneous compute.

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

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.95

Strong JD signal in ML/HPC roles; CUDA is a common requirement for GPU-accelerated training/inference and remains the de facto NVIDIA platform, with broad ecosystem support in PyTorch/TensorFlow.

Vendor & license

(0.95)

Context keywords

GPU parallel computing NVIDIA cuDNN Tensor Cores CUDA Toolkit kernel memory management CUDA streams thrust OpenCL deep learning performance optimization compute capability CUDA-aware MPI

Ambiguity low

CUDA is a specific NVIDIA GPU computing platform; typical JDs won’t confuse it with other distinct 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)

Accelerators and Hardware for ML Catalog dimension db id 58

Library dimension (catalog)

Roles linked in library: ML Engineer
Codec Performance Benchmarking Catalog dimension db id 238

Library dimension (catalog)

Roles linked in library: Video Codec Engineer

Locked dimensions (v3 placement)

GPU Accelerators and Parallel Compute
Reuses catalog slug

Programming and optimization for GPU and other accelerator hardware. CUDA belongs here because it is the primary programming model for writing parallel kernels, managing device memory, and exploiting GPU compute throughput.
Codec Performance Benchmarking
Reuses catalog slug

Measurement and optimization practices for codec implementations on CPU and GPU. CUDA can fit here when the skill is used specifically to accelerate or profile video codec pipelines rather than as general-purpose GPU programming.

Machine Learning Secondary 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 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).

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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  "about_company": null,
  "certifications": [],
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  "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

{
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    },
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    },
    {
      "is_primary": true,
      "skill_name": "AVX"
    },
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      "is_primary": true,
      "skill_name": "ARM"
    },
    {
      "is_primary": true,
      "skill_name": "NEON"
    },
    {
      "is_primary": true,
      "skill_name": "Git"
    },
    {
      "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"
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      "skill_name": "OpenCL"
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        "word_count": 64
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        "bullet_count": 10,
        "heading": "Required Technical Skills",
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        "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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    ],
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        "display_name": "DevOps Engineer",
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    "llm2_fired": false,
    "llm2_reasoning": null,
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027Video Codec Engineer\u0027 (name match, sim 1.00); KRA agrees (0.52)"
  },
  "stage5_updates": {
    "centroid_n_after": 2,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
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        "is_primary": true,
        "queue_id": 1593,
        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
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        "is_primary": true,
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        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
        "skill_name": "AVX",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1595,
        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
        "skill_name": "H.265",
        "status": "pending"
      },
      {
        "is_primary": false,
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        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
        "skill_name": "H.264",
        "status": "pending"
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      {
        "is_primary": false,
        "queue_id": 1597,
        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
        "skill_name": "MPEG-2",
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        "is_primary": false,
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        "skill_name": "OpenCL",
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        "is_primary": false,
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        "role_display_name": "Video Codec Engineer",
        "role_slug": "video-codec-engineer",
        "skill_name": "CUDA",
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        "role_slug": "video-codec-engineer",
        "skill_name": "Neural Networks",
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}

API 2 — extract-details

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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "input_term": "C++",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2688,
      "existing_alias_text": "SSE",
      "input_term": "SSE",
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        "is_also_category": false,
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        "skill_nature": "CONCEPT",
        "slug": "sse",
        "sub_category_id": 1277,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2686,
      "existing_alias_text": "AVX2",
      "input_term": "AVX",
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        "is_also_category": false,
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        "sub_category_id": 1277,
        "typical_lifespan": "EVERGREEN",
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    {
      "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,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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        "is_also_category": false,
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        "skill_nature": "TOOL",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "existing_alias_text": "H.265/HEVC",
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      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "existing_alias_text": "Machine Learning",
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        "is_also_category": false,
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        "skill_nature": "CONCEPT",
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      "matched_via": "alias"
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            "maturity": "deprecated",
            "reasoning": "MPEG-2 is largely superseded in new deployments by H.264/H.265 and AV1; recent job postings rarely list it except for legacy broadcast/DVD systems, indicating low current market demand."
          },
          "skill_id": "mpeg-2",
          "vendor_license": {
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            "license": "unknown",
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            "year_introduced": 1994
          },
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            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
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            ],
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              },
              {
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              }
            ],
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          },
          {
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              }
            ],
            "tentative_id": "d_init_02"
          }
        ],
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        },
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          "parent_skills": [],
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          "requires": [],
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        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
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          "alias_text": "VP9",
          "alias_type": "CANONICAL",
          "id": 2649,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
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      "dimensions": [
        {
          "dimension": {
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          },
          "input_skill": "VP9",
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          "roles_from_db": [
            {
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          ]
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      "new_alias_text": null,
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    },
    {
      "aliases_in_db": [],
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        {
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            "display_name": "React Frontend Development",
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          },
          "input_skill": "OpenCL",
          "llm_role": null,
          "roles_from_db": []
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      ],
      "input_skill": "OpenCL",
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      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
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          },
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          },
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            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
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              "GPU kernel programming",
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              "device memory management",
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            "name": "Parallel GPU Computing",
            "out_of_scope": "codec algorithm design, video compression standards, benchmarking codec throughput, general CPU multithreading, graphics shading APIs like OpenGL or Vulkan rendering pipelines",
            "overlap_flags": [
              {
                "reason": "Both involve accelerator programming, but this dimension is broader and not specific to ML hardware workflows.",
                "with_dim_id": "accelerators-and-hardware-for-ml",
                "with_dim_name": null,
                "with_role": "ML Engineer"
              },
              {
                "reason": "OpenCL is often used to accelerate codec workloads, but benchmarking is about measurement rather than kernel development.",
                "with_dim_id": "codec-performance-benchmarking",
                "with_dim_name": null,
                "with_role": "Video Codec Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "OpenCL",
          "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": "opencl"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
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            "rendering-efficiency",
            "competitive-programming",
            "pytorch",
            "webrtc",
            "async-programming"
          ],
          "requires": [],
          "skill_id": "opencl",
          "suppress_on_match": []
        },
        "skill_id": "opencl",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "OpenCL",
          "reasoning": "OpenCL is best treated as a programming language/API specification for expressing parallel kernels and host code, so it fits the Language type rather than a tool or framework.",
          "skill_id": "opencl",
          "subtype": "parallel_computing_language",
          "type": "Language"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Accelerators and Hardware for ML",
            "id": 58,
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            "source": "db"
          },
          "input_skill": "CUDA",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
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          ]
        },
        {
          "dimension": {
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          },
          "input_skill": "CUDA",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Video Codec Engineer",
              "id": 22,
              "rationale": null,
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            }
          ]
        }
      ],
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      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
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          "skill_nature": "CONCEPT",
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          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
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            "confused_with": [],
            "reasoning": "CUDA is a specific NVIDIA GPU computing platform; typical JDs won\u2019t confuse it with other distinct skills in the catalog."
          },
          "context_keywords": {
            "context_keywords": [
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              "parallel computing",
              "NVIDIA",
              "cuDNN",
              "Tensor Cores",
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              "thrust",
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              "CUDA-aware MPI"
            ]
          },
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            "reasoning": "Strong JD signal in ML/HPC roles; CUDA is a common requirement for GPU-accelerated training/inference and remains the de facto NVIDIA platform, with broad ecosystem support in PyTorch/TensorFlow."
          },
          "skill_id": "cuda",
          "vendor_license": {
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            "license": null,
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          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
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            "exemplar_skills": [
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              "CUDA kernels",
              "GPU memory management",
              "CUDA streams",
              "warp-level programming",
              "shared memory optimization",
              "kernel launch configuration",
              "occupancy tuning"
            ],
            "in_scope": "CUDA, CUDA kernels, device memory management, streams and events, warp-level programming, shared memory, occupancy tuning, GPU kernel launches, cuBLAS, cuDNN, Tensor Core usage",
            "name": "GPU Accelerators and Parallel Compute",
            "out_of_scope": "Model training frameworks and distributed training strategies, which belong to distributed-training-systems; codec-specific profiling and quality analysis, which belong to codec-performance-benchmarking; general cloud provider selection, which belongs to cloud-platforms",
            "overlap_flags": [
              {
                "reason": "CUDA is often used inside multi-GPU training stacks, but that dimension owns the distributed scaling patterns rather than the low-level GPU programming model.",
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                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
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                "with_dim_id": "codec-performance-benchmarking",
                "with_dim_name": null,
                "with_role": "Video Codec Engineer"
              }
            ],
            "tentative_id": "accelerators-and-hardware-for-ml"
          },
          {
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              "video encode acceleration"
            ],
            "in_scope": "CUDA for codec acceleration, GPU profiling of encode/decode pipelines, throughput and latency measurement, memory transfer analysis, kernel timing, NVTX markers, Nsight Systems, Nsight Compute",
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                "with_role": "ML Engineer"
              },
              {
                "reason": "Codec work often involves visual defect diagnosis, but this dimension is about performance measurement rather than quality artifact root-cause analysis.",
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                "with_dim_name": null,
                "with_role": "Video Codec Engineer"
              }
            ],
            "tentative_id": "codec-performance-benchmarking"
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        ],
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        "placed": {
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          ],
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        "relationships": {
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          "related_to": [
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            "pytorch-ddp",
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        "warnings": [
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          "stage4_type_assigner_failed: APIStatusError: Error code: 408 - {\u0027error\u0027: {\u0027code\u0027: \u0027Timeout\u0027, \u0027message\u0027: \u0027The operation was timeout.\u0027}}",
          "stage4_used_fallback_typed"
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      },
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        }
      ],
      "canonical": {
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        "is_also_category": false,
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
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          },
          "input_skill": "Machine Learning",
          "llm_role": null,
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          ]
        },
        {
          "dimension": {
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          },
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          "llm_role": null,
          "roles_from_db": []
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      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
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      "new_alias_text": null,
      "new_skill_meta": null,
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    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Neural Networks",
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      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "x86",
    "MPEG-2",
    "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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