Senior Data Scientist, Data Quality and AI Evaluation
$224k - $431k • US, CA, Santa Clara • Full-time
Posted 1d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
NVIDIA is building the next generation of AI-native autonomous driving architecture, integrating classical safety stacks, foundation models, and scalable AI systems into a unified production platform. This role is for individuals passionate about shaping the future of autonomous systems over the next decade. The work involves the deep integration of classical safety architectures and large-scale AI-driven systems, moving beyond purely classical robotics or end-to-end neural networks.
Responsibilities
- Define quality metrics and evaluation methods for auto-labeling pipelines.
- Build and maintain human-verified datasets for diverse driving conditions and corner cases.
- Analyze large-scale sensor, camera, and annotation data to identify root causes of errors.
- Develop automated approaches for failure clustering and data selection for human correction.
- Build statistically sound audits and experiments to establish release criteria.
- Partner with labeling operations to improve annotation guidelines and reviewer consistency.
Requirements
- Strong foundation in statistics, experimental design, sampling, hypothesis testing, and uncertainty estimation.
- Practical experience evaluating machine learning systems and data quality.
- Proficiency in Python and SQL for analyzing large datasets and building analytical workflows.
- Understanding of precision, recall, class imbalance, selection bias, data leakage, and scenario-based evaluation.
- Ability to distinguish reference-label defects from model errors and translate quality concerns into actionable recommendations.
- Strong communication and teamwork skills, with experience collaborating across different teams.
- 12+ years of industry experience with a Bachelor's degree in a quantitative field, or equivalent experience.
- Experience with autonomous driving, robotics, or computer vision, particularly camera and LiDAR data, 2D/3D annotations, multi-object tracking, occupancy, or temporal/cross-sensor consistency.
- Experience designing human-in-the-loop labeling and audit workflows, measuring annotator agreement, or maintaining versioned benchmarks.
Benefits
- Equity
- Benefits