10 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Build and optimize synthetic-data tools for deep-learning DRIVE networks, including simulated lidar, radar, camera/RGB-D, annotations, object tracks, world models, segmentation, depth, scene semantics, and sensor metadata.
- Develop lidar and radar simulation workflows using NuRec reconstructed worlds and Cosmos-generated environments, including calibration, material response, geometry, noise modeling, and scenario variation.
- Develop Cosmos world-model capabilities for scenario generation, novel-view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation.
- Translate perception, planning, and deep-learning network requirements into synthetic-data features and tools.
- Create dataset-quality assessments and synthetic-real comparison procedures for realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer.
- Set up, profile, supervise, and debug large-scale simulation pipelines across data-center or cloud environments and distributed services.
Requirements
- Bachelor’s or master’s degree in Computer Science, Electrical Engineering, Computer Engineering, Applied Math, Physics, or a related field, or equivalent experience.
- At least 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.
- Strong Python and C++ skills and experience building, debugging, profiling, and maintaining production-quality systems on Linux.
- Strong foundations in linear algebra, geometry, and probability.
- Familiarity with synthetic-data annotations, data formats, dataset curation, data augmentation, and perception-model evaluation workflows.
- Practical understanding of deep-learning workflows and modern ML tooling sufficient to translate network needs into synthetic-data requirements and measurable quality criteria.
- Experience with scalable engineering workflows involving Git, Docker, Kubernetes, distributed storage, and deployment in data centers or cloud environments.
- Preferred experience with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim, autonomous-driving simulation, NuRec reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, occupancy networks, lidar or radar simulation, domain randomization, long-tail scenario mining, sim-to-real transfer, OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or autonomous-vehicle safety validation.
Benefits
- Eligible for equity and benefits.
- Base salary varies by location, experience, and other objective job-related factors, with stated ranges for Level 4 and Level 5.
- Applications are accepted at least until November 3, 2026.
- This posting is for an existing vacancy.
Categories
ML EngineeringRobotics
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.