Note: The job is a remote job and is open to candidates in USA. NVIDIA is building autonomous driving technology and operates an AV Evaluation team responsible for metrics, golden datasets, and closed-loop evaluation workflows. The Senior Software Engineer will own the evaluation flywheel end to end, including its architecture, quality, adoption, dataset pipelines, metric measurement, and tooling while partnering with engineering, infrastructure, and AI model development teams.
Responsibilities
- Owning the eval flywheel's strategy and architecture: how road and simulation driving data becomes curated golden datasets, how metrics are measured against them (precision/recall), and how those results earn lasting trust with the teams that depend on them
- Setting the standard for evaluation quality: golden dataset curation, versioning, and health; metric performance measurement; and release processes that keep results dependable as the system evolves
- Building the tooling that helps our metric developers iterate quickly: self-serve dataset pipelines, metric performance measurement, and quality reporting used every day by the team and our partners
- Partnering with senior engineers and leaders across test engineering, behavior planning, and infrastructure — setting expectations, working through trade-offs, and being the voice of evaluation quality in cross-team decisions
- Working directly with AI model developers so evaluation iteration speed becomes an advantage for the whole program, including our push into learned, VLM-based evaluation
Skills
- A track record of independent execution and technical leadership: finding the highest-leverage problem, driving it across team boundaries, and delivering without waiting to be asked
- Clear, proactive communication with engineers and senior leaders alike, at a fast pace
- BS or MS in Computer Science, Robotics, or a related field (or equivalent experience)
- 12+ years building software, with significant time in autonomous vehicles, robotics, or large-scale ML systems
- Deep experience evaluating ML or robotic systems: metric design, ground-truth and golden dataset curation, precision/recall methodology, and the data pipelines behind them
- Strong Python and data engineering skills for production-scale pipelines
- Experience building an evaluation flywheel before — dataset curation, metric measurement, developer tooling — and the story of how it changed model development velocity
- Closed-loop simulation evaluation for autonomous driving, and the realism questions that come with it
- Applying LLMs or VLMs to evaluation, or productionization the infrastructure behind them
- A history of earning trust for metrics across skeptical partner teams
Benefits
- You will also be eligible for equity.
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