NVIDIA's AV Eval team is building the next generation of driving behavior evaluation - moving beyond hand-crafted rules to learned evaluation using LLMs, VLMs, and agentic workflows. You'll define how we measure whether an autonomous vehicle drives well, building systems that bridge ML research and production evaluation. You'll ship systems that run at scale on real-world driving data and produce metrics that block or green-light software releases. In this role you will get to work on next-gen AV evaluation and create a direct impact on vehicle safety and shipping decisions. Join a new team being built from scratch - high ownership, high visibility to NVIDIA AV leadership
What You will be doing:
- Design and build learned evaluation pipelines that assess driving behavior using LLMs, VLMs, and multimodal models
- Develop agentic workflows that chain model inference, retrieval, and structured reasoning to evaluate complex driving scenarios
- Define evaluation-of-evaluation methodology - how do we know our learned evaluators are correct?
- Build golden-set frameworks and calibration loops for learned metrics
- Partner with AML (Alpamayo Logos) teams on model-specific eval needs (e.g., COT prediction quality, AML regression coverage)
- Instrument evaluation systems with robust experiment tracking, A/B comparison tooling, and model versioning
- Contribute to the team's transition from rule-based to learned evaluation: identify metrics and analyzers that are candidates for ML replacement and build the alternatives
What we need to see:
- PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
- Hands-on experience building LLM/VLM-based pipelines - fine-tuning, prompt engineering, retrieval-augmented generation, chain-of-thought
- Track record of shipping ML systems to production (not just prototyping or publishing)
- Strong software engineering fundamentals - you write clean, tested, reviewable code in Python and C++
- Experience with evaluation methodology: precision/recall, inter-rater reliability, calibration, annotation pipelines
- Comfort with large-scale data processing (Spark, Dask, or similar)
- Strong Python skills. Experience with PyTorch or JAX. Comfortable with GPU-based training workflows.
Ways to stand out from the crowd:
- Autonomous driving, robotics, or safety-critical domain experience
- Familiarity with driving behavior taxonomies (cut-ins, hard braking events, lane-keeping metrics, scenario-based evaluation)
- Experience with video understanding models or multi-modal evaluation. Knowledge of agentic AI frameworks (LangChain, DSPy, CrewAI, or custom)
- Track record of influencing technical direction across team boundaries
- Experience with LLM/VLM fine-tuning or application development
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until April 19, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.