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careers@nuvacore.aiAI Methodology Engineer
Full-time · US / Canada / India
Nuvacore is building a ground-up high performance, low-power CPU for next-generation compute workloads. We are seeking an AI Methodology Engineer to establish the pervasive use of AI within the design verification (DV) team. You will leverage AI — LLMs and machine learning — to accelerate testbench development, stimulus generation, coverage closure, and debug across the team. The goal is to build the AI tools and methodologies that make every engineer more productive — amplifying what the whole team can do, long before silicon.
THE ROLE
- AI Verification Environment: Build and deploy an AI verification environment that accelerates testbench development — automating stimulus generation and coverage closure to amplify what each engineer can do.
- Agents & Models: Develop and integrate AI agents and ML models into the verification toolchain to automate intent-to-testbench workflows.
- AI Debug Assistants: Create AI-based debug assistants that analyze failures, categorize bugs, and suggest fixes, with a human in the loop.
- Applied Research: Research and apply AI approaches — LLMs, machine learning, and reinforcement learning — to the state-space-explosion problem in verification.
- Tool & Vendor Integration: Partner with design-automation teams and EDA vendors so the AI solutions deliver end-to-end efficiency across the flow.
- Adoption & Enablement: Champion AI across design verification — drive its pervasive adoption and train engineers on the tools and human-in-the-loop methodologies.
REQUIREMENTS — MUST HAVE
- Degree in Electrical/Computer Engineering, Computer Science, or equivalent practical experience.
- 3+ years of hands-on pre-silicon verification experience.
- Strong knowledge of functional verification — testbenches, stimulus, coverage, and debug.
- Hands-on experience applying AI/ML — LLMs and/or machine learning — to real problems.
- Proficiency in C/C++/Rust and Python, plus scripting, for building tools and models.
- Drive to automate and redefine manual verification workflows.
REQUIREMENTS — nice to HAVE
- AI for EDA — building or using AI tools for hardware verification (automated coverage, log analysis, or bug prediction).
- Reinforcement learning or LLM-agent development.
- Experience with the verification toolchain and EDA flows, from RTL to sign-off.
- Constrained-random verification and coverage-driven closure.
- ML infrastructure — training pipelines, data, and model deployment.
- Mentoring and driving methodology adoption across teams.