Senior ML Evaluation Engineer – Autonomous Vehicles

🕒 April 17

🏄 California, District of Columbia, +2 more states – Remote

🏄 California – Remote 🏛️ District of Columbia – Remote 🌲 North Carolina – Remote ☕ Washington – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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.

🏖️ Benefits

• equity • benefits