Senior Synthetic Data Engineer – Autonomous Driving

This is a fully remote position, open to applicants in California, +3 more states.

📋 Description

• Create, implement, and enhance tools for generating synthetic data used in training DRIVE deep learning networks.

• Design lidar and radar sensor simulation workflows for NuRec reconstructed driving environments and Cosmos-generated scenarios.

• Construct Cosmos world models to facilitate controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality assessment, regression detection, and controllability evaluation.

• Collect requirements for perception, planning, and deep learning networks, ensuring alignment with synthetic data and sensor simulation capabilities.

• Innovate new tools and enhance performance in areas where capability gaps are identified.

• Create assessments for dataset quality and procedures for comparing synthetic and real data.

• Assess sensor realism, annotation quality, distribution coverage, scenario diversity, and the transferability from simulation to real-world applications.

• Establish, profile, and manage large-scale NuRec, Cosmos, and sensor simulation pipelines within data center or cloud environments.

• Troubleshoot systems that encompass sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and autonomous-driving tasks.

• Work in collaboration with technical leaders in autonomous driving, NuRec, Cosmos, and sensor simulation.

⛳️ Requirements

• B.S. or M.S. degree in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or a similar field (or equivalent experience).

• A minimum of 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 positions.

• Proficiency in Python and C++ programming languages.

• Experience in building, debugging, profiling, and maintaining production-quality systems on Linux platforms.

• Strong mathematical background in linear algebra, geometry, and probability.

• Familiarity with synthetic data annotations, data formats, dataset curation, data augmentation, and evaluation workflows for training and validating perception models.

• Knowledge of deep learning workflows and contemporary machine learning tools.

• Ability to effectively translate network requirements into synthetic data needs and measurable quality standards.

• Experience with Git, Docker, Kubernetes, CI/CD, distributed storage, and deployment in data center or cloud settings.

• Practical experience with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, or autonomous-driving simulation and validation pipelines is a plus.

• Experience in NuRec world reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, or occupancy networks is advantageous.

• Expertise in deep lidar or radar simulation is beneficial.

• Experience in 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 a plus.

🏝️ Benefits

• Equity

• Comprehensive benefits package