Senior ML Evaluation Engineer - Autonomous Vehicles

NVIDIA is a leading technology company developing groundbreaking solutions in areas such as Artificial Intelligence and Autonomous Vehicles. The Senior ML Evaluation Engineer will be responsible for designing and building learned evaluation pipelines for autonomous driving behavior, using advanced machine learning techniques and ensuring the accuracy of evaluation methodologies.

Responsibilities:

• 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

Requirements:

• 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
• 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