Senior Synthetic Data Engineer - Autonomous Driving
$184k - $357k • Remote • US, CA, Santa Clara
Posted 10d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking a Senior Synthetic Data Engineer to join the DRIVE team and contribute to automotive innovation. This role involves collaborating with technical leaders in autonomous driving and simulation to design and develop a simulation environment that advances autonomous vehicle technology. The position focuses on creating synthetic data crucial for training deep learning models used in autonomous driving systems, enhancing the reliability and speed of vehicle development through advanced simulation techniques.
Responsibilities
- Build, implement, and optimize tools for generating synthetic data (lidar, radar, camera, etc.) for training deep learning DRIVE networks.
- Develop lidar and radar sensor simulation workflows integrated with NuRec and Cosmos environments.
- Enhance the Cosmos world model for improved scenario generation, novel view synthesis, and scene completion.
- Gather requirements from perception, planning, and deep learning teams and develop corresponding synthetic data and simulation features.
- Create dataset quality assessments and sim-to-real comparison procedures to evaluate sensor realism and data diversity.
- Set up, profile, and manage large-scale simulation pipelines in data center or cloud environments.
- Debug complex, cross-stack systems involving sensors, models, simulation runtime, and downstream workloads.
Requirements
- B.S. or M.S. in a relevant technical field (or equivalent experience).
- 8+ years of experience in computer graphics, computer vision, autonomous driving, sensor simulation, neural rendering, or related software engineering roles.
- Strong Python and C++ skills for production-quality systems on Linux.
- Solid mathematical foundation in linear algebra, geometry, and probability.
- Familiarity with synthetic data annotations, formats, curation, augmentation, and evaluation workflows for perception models.
- Practical understanding of deep learning workflows and ML tooling to translate network needs into data requirements.
- Experience with scalable engineering workflows including Git, Docker, Kubernetes, CI/CD, and distributed storage in cloud or data center environments.
Benefits
- Equity
- Benefits