Senior Synthetic Data Engineer – Autonomous Driving
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Job Description
Senior Synthetic Data Engineer developing synthetic data, sensor simulation, and Cosmos/NuRec world-model systems. Advancing NVIDIA’s autonomous-driving platform through scalable simulation and validation.
Responsibilities:
- Build, implement, and optimize tools to generate synthetic data for training DRIVE deep learning networks
- Develop lidar and radar sensor simulation workflows for NuRec reconstructed driving worlds and Cosmos-generated environments
- Develop Cosmos world models for controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation
- Gather perception, planning, and deep learning network requirements and align them with synthetic data and sensor simulation capabilities
- Develop new tools and improve performance where capability gaps exist
- Develop dataset quality assessments and synthetic-real comparison procedures
- Evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer
- Set up, profile, and supervise large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments
- Debug systems spanning sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and autonomous-driving workloads
- Collaborate with technical leaders in autonomous driving, NuRec, Cosmos, and sensor simulation
Requirements:
- B.S. or M.S. in Computer Science, Electrical Engineering, Computer Engineering, Applied Math, Physics, or a related field (or equivalent experience)
- 8+ years of experience in computer graphics, computer vision, autonomous driving, sensor simulation, neural rendering, physically-based sensor modeling, synthetic data generation, or closely related software engineering roles
- Strong Python and C++ skills
- Experience building, debugging, profiling, and maintaining production-quality systems on Linux
- Solid mathematical foundation in linear algebra, geometry, and probability
- Familiarity with synthetic data annotations, data formats, dataset curation, data augmentation, and evaluation workflows for perception model training and validation
- Familiarity with deep learning workflows and modern ML tooling
- Practical understanding sufficient to translate network needs into synthetic data requirements and measurable quality criteria
- Experience with Git, Docker, Kubernetes, CI/CD, distributed storage, and deployment in data centers or cloud environments
- Practical experience with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, or autonomous-driving simulation and validation pipelines is advantageous
- Experience in NuRec world reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, or occupancy networks is advantageous
- Deep lidar or radar simulation expertise is advantageous
- Experience developing synthetic data pipelines for autonomous driving, closed-loop simulation, domain randomization, long-tail scenario mining, or sim-to-real transfer is advantageous
- Familiarity with autonomous vehicle data pipelines, OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation is advantageous
Benefits:
- Equity
- Benefits
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