This role pays more than
75%
of similar roles. Most pay
$158,662–$235,750
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$197k
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Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
892 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 870 roles with salary data.
The Senior Synthetic Data Engineer joins the DRIVE team to develop simulation environments for autonomous vehicle technology. This role involves building and optimizing tools to generate synthetic data for deep learning networks, including lidar, radar, camera/RGB-D, bounding boxes, and scene semantics. The engineer will develop sensor simulation workflows using NuRec and Cosmos, focusing on noise modeling, geometry handling, and scenario variation. Key responsibilities include creating dataset quality assessments, evaluating sim-to-real transfer, and managing large-scale pipelines in cloud or data center environments. Candidates must possess strong Python and C++ skills, a solid mathematical foundation in linear algebra and geometry, and experience with Linux systems. Required expertise includes computer vision, neural rendering, and scalable engineering workflows involving Git, Docker, Kubernetes, and CI/CD to solve complex challenges in autonomous driving simulation and perception model training.
Build and optimize tools to generate synthetic data for training deep learning DRIVE networks including lidar, radar, and camera data.
Develop sensor simulation workflows for NuRec reconstructed worlds and Cosmos-generated environments including noise modeling and geometry handling.
Develop Cosmos world models for controllable scenario generation, novel view synthesis, and trajectory extrapolation.
Translate perception and planning requirements into specific synthetic data features and measurement criteria.
Create dataset quality assessments to evaluate sensor realism, annotation quality, and sim-to-real transfer performance.
Manage and profile large-scale simulation pipelines in data center or cloud environments.
Debug cross-stack systems including reconstruction models, GPU workloads, and distributed data services.
What we're looking for
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.
Strong Python and C++ skills for 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, and evaluation workflows for perception model training.
Familiarity with deep learning workflows and modern ML tooling to translate network needs into synthetic data requirements.
Experience with scalable engineering workflows including Git, Docker, Kubernetes, CI/CD, distributed storage, and cloud or data center deployment.
Experience in neural rendering, 3D Gaussian Splatting, NeRFs, or advanced lidar/radar simulation (preferred).
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model