Architect – AI-Powered Performance Verification Automation

Overview

Design, develop and deploy AI and automation solutions that accelerate hardware performance verification cycles, improve coverage and reduce manual effort across project lifecycles.

What you'll do

• Automate verification workflows by building AI/ML-based tools that generate, triage, and analyse performance test cases and results.
• Develop intelligent agents that 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 you'll need

• 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.
• 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.

Nice to have

• Familiarity with C/C++ and SystemVerilog/UVM.
• 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.

Details

• Location: Bengaluru, India.
• Work mode: Hybrid.