Senior Software Engineer, Evaluation Flywheel – Autonomous Vehicles

NVIDIA

Senior Software Engineer responsible for the eval flywheel architecture and quality in NVIDIA's self-driving technology. Collaborating with teams to ensure trust and high standards in evaluation processes.

Posted 7/28/2026full-timeSanta Clara • California, District of Columbia, North Carolina, Washington • 🇺🇸 United StatesSenior💰 $224,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and evaluating machine learning systems, with a focus on golden dataset curation, metric performance measurement, and data pipeline development. Strong collaboration with cross-functional teams to ensure evaluation quality and system reliability.

Highest-signal resume keywords

Machine Learning EvaluationGolden Dataset CurationMetric Performance MeasurementPython ProgrammingData Engineering

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Hard Skills

Machine LearningMetric DesignGround-Truth CurationPrecision/Recall MethodologyData Pipeline DevelopmentSoftware DevelopmentRoboticsAutonomous VehiclesLarge-Scale ML SystemsQuality Reporting

Soft Skills

CollaborationCommunicationStrategic ThinkingProblem Solving

Tools & Technologies

Data PipelinesSelf-Serve Dataset ToolsQuality Reporting Tools

Industry Keywords

Autonomous VehiclesRoboticsMachine Learning SystemsEvaluation QualityVersioningRelease Processes

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
BS or MS in Computer Science, Robotics, or a related field (or equivalent experience).

Benefits

Comp & perks

equity
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