Senior Synthetic Data Engineer – Autonomous Driving

NVIDIA

. Build, implement, and optimize tools to generate synthetic data for training DRIVE deep learning networks .

Posted 9/21/2026full-timeRemote • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and optimizing synthetic data generation tools and sensor simulation workflows for autonomous driving applications, with strong proficiency in Python and C++. Demonstrated ability to evaluate dataset quality and align deep learning network requirements with simulation capabilities.

Highest-signal resume keywords

Python ProgrammingC++ ProgrammingSensor SimulationDeep Learning WorkflowsSynthetic Data Generation

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Hard Skills

Computer GraphicsComputer VisionNeural RenderingPhysically-Based Sensor ModelingDataset CurationData AugmentationLinear AlgebraGeometryProbabilityDebugging and Profiling

Tools & Technologies

LinuxGitDockerKubernetesCI/CDNVIDIA NuRecCosmosDistributed StorageCloud EnvironmentsSimulation Pipelines

Industry Keywords

Autonomous DrivingSynthetic DataSim-to-Real TransferOpenDRIVEHD MapsVehicle DynamicsAV Safety ValidationLong-Tail Scenario MiningDomain Randomization3D Gaussian Splatting

Tech Stack

Tools & technologies

CloudDockerKubernetesLinuxPythonC++

About the role

Key responsibilities & impact

Build, implement, and optimize tools to generate synthetic data for training DRIVE deep learning networks
Develop lidar and radar sensor simulation workflows for NuRec reconstructed driving worlds and Cosmos-generated environments
Develop Cosmos world models for controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation
Gather perception, planning, and deep learning network requirements and align them with synthetic data and sensor simulation capabilities
Develop new tools and improve performance where capability gaps exist
Develop dataset quality assessments and synthetic-real comparison procedures
Evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer
Set up, profile, and supervise large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments
Debug systems spanning sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and autonomous-driving workloads
Collaborate with technical leaders in autonomous driving, NuRec, Cosmos, and sensor simulation

Requirements

What you’ll need

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, physically-based sensor modeling, synthetic data generation, or closely related software engineering roles
Strong Python and C++ skills
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
Practical understanding 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 centers or cloud environments
Practical experience with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, or autonomous-driving simulation and validation pipelines is advantageous
Experience in NuRec world reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, or occupancy networks is advantageous
Deep lidar or radar simulation expertise is advantageous
Experience developing synthetic data pipelines for autonomous driving, closed-loop simulation, domain randomization, long-tail scenario mining, or sim-to-real transfer is advantageous
Familiarity with autonomous vehicle data pipelines, OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation is advantageous

Benefits

Comp & perks

Equity
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