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About This Role
- Key Responsibilities Agentic QA Automation: Help design and build automated QA flows using LLMs/Agents to catch, triage, and report framework and hardware bugs with minimal manual effort.
- Benchmarking and Validation: Run, maintain, and expand end-to-end (E2E) benchmarks, unit tests (UT), and microbenchmarks for PyTorch, vLLM, and SGLang.
- Hardware and Stack Coverage: Validate latency, throughput, and functionality across different hardwares; track and reproduce performance regressions.
- Issue Tracking and CI/CD: Maintain test tracking sheets, document bug reproduction steps, and integrate tests into CI/CD pipelines.
Qualifications
- Basic Qualifications Education: Bachelor, Master, Software Engineering, or a related field Programming: Proficient in Python AI/ML Fundamentals: Basic understanding of Deep Learning concepts, Transformer models, and standard model execution flow.
- Linux and Tools: Comfortable working in Linux environments, using shell scripting (Bash), and version control with Git.
- Preferred Skills (Nice to Have) Hands-on experience or coursework with PyTorch, vLLM, or SGLang.
- Experience building basic LLM applications, scripts, or agent workflows (e.g., using LLM APIs or frameworks like LangChain).
- Exposure to basic testing concepts (UT, regression testing) or CI/CD tools (e.g., GitHub Actions).
- Familiarity with Docker or profiling tools (e.g., PyTorch Profiler).
Tools & Skills
Languages
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Specialisation
Open roles at Intel
627 positions
Job ID
/job/PRC-Shanghai/AI-Framework-Engineer---QA---Benchmarking_JR0286411
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