Senior Synthetic Data Engineer - Autonomous Driving

NVIDIA is building simulation-driven capabilities for autonomous driving, and this role focuses on turning reconstructed driving worlds into high-quality synthetic data at production scale. As a Senior Synthetic Data Engineer, you will design simulation environments and synthetic data pipelines using NVIDIA Omniverse NuRec and Cosmos, with an emphasis on multi-sensor realism, dataset quality assessment, and robust pipeline operations.

This position is based in Santa Clara, CA on an onsite basis, with an expected compensation range of USD 184,000 - 356,500 per year. Experience requirements include 8+ years in relevant engineering and technical domains.

What you’ll do

• Build, implement, and optimize tools to generate synthetic data for training different DRIVE deep learning networks, including simulated lidar, radar, camera/RGB-D, bounding boxes, object tracks, world models, segmentation, depth, scene semantics, and sensor metadata.
• Develop lidar and radar sensor simulation workflows that run on NuRec reconstructed driving worlds and Cosmos-generated environments, covering sensor placement, calibration, material response, geometry handling, noise modeling, and scenario variation.
• Develop a Cosmos world model to improve world generation, including controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation.
• Gather perception, planning, and deep learning DRIVE network requirements, then map them to existing synthetic data and sensor simulation features, adding new tools or improving performance when gaps are found.
• Create dataset quality assessments and synthetic-real comparison procedures to evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer for autonomous driving.
• Set up, profile, and oversee large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments.
• Debug end-to-end, cross-stack systems spanning sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and downstream autonomous-driving workloads.

Required qualifications

• 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, or physically-based sensor modeling, synthetic data generation, or closely related software engineering roles.
• Strong skills in Python and C++, including 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, with enough practical depth to translate network needs into synthetic data requirements and measurable quality criteria.
• Experience with scalable engineering workflows, including Git, Docker, Kubernetes, CI/CD, distributed storage, and deployment in data centers or cloud environments.

Technologies

• Python, C++, Linux, Git, Docker, Kubernetes, CI/CD
• NuRec, Cosmos, NVIDIA Omniverse
• Distributed storage, cloud, data center

Equity and benefits

You will also be eligible for equity and benefits.

Ways to stand out

• Practical experience working directly with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, or autonomous-driving simulation and validation pipelines.
• Experience in NuRec world reconstruction, neural rendering, including 3D Gaussian Splatting, NeRFs, or occupancy networks.
• Deep lidar or radar simulation expertise, including ray tracing or ray casting, reflectance and intensity modeling, Doppler, radar cross-section, weather effects, occlusion, and sensor-specific noise models.
• Experience developing synthetic data pipelines for autonomous driving, including closed-loop simulation, domain randomization, long-tail scenario mining, or sim-to-real transfer.
• Familiarity with autonomous vehicle data pipelines and formats such as OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation.