1 day ago
Base Salary
$184k - $357k/yr
Responsibilities
- Build, implement, and optimize synthetic-data generation tools for deep-learning autonomous-driving networks, including lidar, radar, camera, 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 controllable 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 and sensor-simulation capabilities.
- Create dataset-quality assessments and synthetic-versus-real comparison procedures for sensor realism, annotation quality, coverage, diversity, and sim-to-real transfer.
- Set up, profile, supervise, and debug large-scale NuRec, Cosmos, and sensor-simulation pipelines across data centers and cloud environments.
Requirements
- Bachelor’s or master’s degree in computer science, electrical engineering, computer engineering, applied mathematics, 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 related software engineering.
- Strong Python and C++ skills and experience building, debugging, profiling, and maintaining production-quality systems on Linux.
- Strong 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.
- Practical understanding of deep-learning workflows and modern machine-learning tooling 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-center or cloud environments.
- Preferred experience with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, autonomous-driving simulation, neural rendering, 3D Gaussian Splatting, NeRFs, occupancy networks, lidar or radar simulation, domain randomization, 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 comparable employee pay.
- Applications will be accepted at least until September 25, 2026.
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.