What you'll be doing
Automate verification workflows by building AI/ML-based tools that generate, triage, and analyse performance test cases and results
Develop intelligent agents that can identify performance regressions, root-cause failures, and recommend corrective actions
Integrate LLM-based assistants into existing verification infrastructure to enable natural-language querying of results, specs, and coverage data
Design data pipelines to collect, curate, and label verification data for model training and continuous improvement
Collaborate with verification engineers to understand pain points, define automation priorities, and validate AI-driven solutions against real-world workflows
Establish metrics and dashboards to measure automation impact (cycle time reduction, coverage improvement, engineer productivity)
Stay current with state-of-the-art techniques in generative AI, reinforcement learning, and formal methods as they apply to hardware verification
What we need to see
B.Tech/M.Tech/PhD in Electrical Engineering, Computer Science, or a related field
3+ years of experience in hardware verification, performance validation, or EDA tool development
Strong programming skills in Python; familiarity with C/C++, SystemVerilog/UVM is a plus
Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn) or LLM APIs (OpenAI, NVIDIA NIM/NeMo)
Understanding of performance verification methodologies (benchmarking, profiling, regression analysis)
Experience with CI/CD pipelines and infrastructure automation
Ways to stand out from the crowd
Experience applying ML to EDA or verification problems (e.g., coverage closure, bug prediction, test generation)
Familiarity with RAG architectures, prompt engineering, and agentic AI frameworks (LangChain, CrewAI, etc.)
Knowledge of NVIDIA GPU/SoC architecture or similar complex hardware platforms
Published work or patents in AI-for-verification or related domains
#LI-Hybrid
