AI Validation Engineer embedded systems

AI Validation Engineer embedded systems:

Key Responsibilities:

AI/ML Validation & Quality Ownership on Ubuntu & Yocto OS Variants

• Lead the Team & Exeuctie validation efforts for complex AI/ML compute stacks across multiple platforms and environments

• Define, develop, and execute comprehensive validation plans, test strategies, and methodologies for AI software and system pipelines

• Own the defect lifecycle, including reporting, triage, root cause analysis, and closure

• Ensure validation coverage across functional, performance, stress, regression, and scalability testing

End to End AI Pipeline Validation

• Validate end to end AI workflows, including:

Validate end to end ROCmLibraries

o Model training, conversion, and optimization (e.g., PyTorch → ONNX)

o Inference runtimes (ONNX Runtime, TensorRT, ROCm/HIP, OpenVINO)

o Compilers and toolchains (TVM, Vitis AI, XDNA, XLA)

o Kernel execution, memory transfers, and inference accuracy

• Validate correctness, stability, and performance of AI workloads on Linux based environments (Ubuntu, Yocto)

AI Benchmarking, Profiling & Optimization

• Define and execute AI benchmarking strategies for training and inference workloads across hardware platforms

• Perform performance profiling and analysis of AI models to identify compute, memory, and latency bottlenecks

• Collaborate with compiler, runtime, and hardware teams to drive model level and system level optimizations

• Validate performance improvements across model architectures, batch sizes, precision modes (FP32, FP16, INT8), and execution paths

• Ensure performance regressions are detected early and performance KPIs are met for production releases

ML Frameworks & Compute Stack Expertise

• Validate integration, functionality, and performance of AI frameworks including PyTorch, TensorFlow, and ONNX Runtime

• Execute and validate AI/ML workloads on compute stacks such as ROCm/HIP, CUDA, OpenCL, and heterogeneous accelerators

• Analyze performance impact of framework, compiler, and runtime changes on real world AI models

Automation & Tooling

• Design and implement Python based validation and benchmarking frameworks

• Develop reusable automation for test execution, benchmarking, profiling, result analysis, and reporting

• Drive continuous improvements in validation efficiency, benchmarking scalability, and coverage

Technical Leadership & Collaboration

• Work closely with architecture, compiler, runtime, driver, and hardware teams to debug performance and functional issues

• Provide technical guidance and mentorship to junior engineers and validation teams

• Collaborate effectively with global, cross functional teams across time zones

• Communicate validation, benchmarking results, performance risks, and quality metrics clearly to stakeholders

Required Skills & Qualifications:

8 to 12 years Experience in AI Validation & Strong Python & Scripting knowledge

Technical Expertise

• Strong background in machine learning fundamentals, including:

o Deep learning

o Large Language Models (LLMs)

o Recommender systems

• Strong experience with ROCm concepts and ROCm validation

• Hands on experience with AI benchmarking, profiling, and performance optimization of AI models

• Prior experience validating AI/ML compute stacks, including:

o HIP, CUDA, OpenCL

o OpenVINO

o TensorFlow / PyTorch integrations

• Experience validating end to end AI pipelines, including model conversion, inference runtimes, compilers, kernel execution, and accuracy

• Strong Python programming skills for automation, benchmarking, and validation

• Experience profiling AI workloads to optimize throughput, latency, and resource utilization

• Strong knowledge of Linux environments, specifically Ubuntu and Yocto

Validation, Quality & Process

• Strong experience with software validation processes, defect management, and SDLC

• Experience with production level quality assurance practices and performance driven validation

• Strong focus on test quality, performance coverage, reproducibility, and release readiness

• Ability to work independently with strong ownership of deliverables

Soft Skills

• Excellent problem solving abilities with a performance driven and analytical mindset

• Strong verbal and written communication skills

• Proven ability to work effectively with global and cross functional teams

Preferred Qualifications:

• Experience benchmarking and optimizing AI workloads on heterogeneous hardware platforms (CPU, GPU, AI accelerators)

• Exposure to performance tuning of large scale models, including memory optimization and mixed precision execution

• Experience contributing to or working with open source AI and performance focused projects

Education:

Bachelor's or Master's degree in AI/ML, Robotics, Electronics, Computer Science, or a related field.