Senior Synthetic Data Engineer - Autonomous Driving

NVIDIA•Us, CA

About The Position

Autonomous vehicles are redefining the way we live, work, and play—creating safer and more efficient roads. These ground-breaking benefits require substantial computational horsepower and large-scale production software expertise. Tapping into decades-long experience in high-performance computing, imaging, and AI, NVIDIA has built a software-defined, end-to-end platform for the transportation industry that enables continuous improvement and deployment through over-the-air updates. It delivers everything needed to develop autonomous vehicles at scale. Simulation allows us to test an autonomous vehicle in nearly infinite conditions and scenarios. This happens before the vehicle reaches the road. It speeds up development and improves the system's reliability. NVIDIA Omniverse NuRec and Cosmos are starting a new chapter for autonomous-driving simulation. NuRec converts real-world sensor data into high-fidelity, simulation-ready driving environments. Cosmos world models generate, complete, and evaluate dynamic driving scenes, novel viewpoints, long-tail trajectories, and controlled scenario variations beyond the original sensor path. We seek a Senior Synthetic Data Engineer to join the NVIDIA DRIVE team and contribute to automotive innovation. In this important role, you will collaborate with technical leaders in autonomous driving, NuRec, Cosmos, and sensor simulation. Together, you will design and develop a simulation environment that pushes forward autonomous vehicle technology.

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, or physically-based sensor modeling, synthetic data generation, or closely related software engineering roles.
Strong Python and C++ skills, with experience building, debugging, profiling, and maintaining production-quality systems on Linux.
Solid mathematical foundation in linear algebra, geometry, 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 understanding 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.

Nice To Haves

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, 3D Gaussian Splatting, NeRFs, or occupancy networks.
Deep lidar or radar simulation expertise, such as 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, closed-loop simulation, domain randomization, long-tail scenario mining, or sim-to-real transfer.
Familiarity with autonomous vehicle data pipelines, OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation.

Responsibilities

Build, implement, and optimize tools to generate synthetic data for training different deep learning DRIVE 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 against NuRec reconstructed driving worlds and Cosmos-generated environments, including sensor placement, calibration, material response, geometry handling, noise modeling, and scenario variation.
Develop Cosmos world model for better world generation, encompassing controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation.
Collect perception, planning, and deep learning DRIVE network requirements and match them to current synthetic data and sensor simulation features.
Develop new tools and improve performance when gaps are found.
Develop dataset quality assessments and synthetic-real comparison procedures that evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer for autonomous driving.
Set up, profile, and supervise large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments.
Debug cross-stack systems spanning sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and downstream autonomous-driving workloads.