Senior Simulation & Synthetic Data Generation Engineer – Self-Driving
Indexed description
Synthetic data and simulation are critical to scaling self-driving technology. In this role, you will research, develop, and deploy state-of-the-art techniques in computer graphics, computer vision, and machine learning to generate high-quality data for training and validating the self-driving stack. You will help shape the future of synthetic data generation pipelines and simulation frameworks, enabling new sensing model capabilities and accelerating development of safe autonomous vehicles.
- Design and implement generative AI–driven pipelines for synthetic data generation, including 3D scenes, sensor simulation, and realistic traffic scenarios
- Develop synthetic datasets to train and validate perception and sensing ML models, ensuring diversity and realism across scenarios
- Collaborate with perception , prediction, mapping and planning teams to integrate synthetic data into model training and evaluation workflows
- Research and apply advanced methods in computer vision, computer graphics, and generative modeling (e.g., diffusion models, NeRF , Gaussian splatting)
- Benchmark and validate synthetic data against real-world distributions, ensuring effectiveness in closing domain gaps
- Build scalable infrastructure for large-scale synthetic data generation and management
- Work closely with other engineering and research experts to align synthetic data strategy with broader ML and autonomy goals
- Look for high-impact findings and propose improvements to synthetic data pipelines that unlock new model capabilities
- MSc/PhD in Computer Science, Computer Graphics, Robotics, or a related field with 5+ years of industry experience
- Strong background in computer vision, computer graphics, or machine learning
- Experience developing ML pipelines that incorporate synthetic or simulated data
- Proficiency in Python and C++ with experience in ML frameworks such as PyTorch or TensorFlow
- Familiarity with 3D graphics frameworks, game engine s , or robotics simulators (e.g., CARLA, Gazebo)
- Knowledge of generative modeling techniques (e.g., diffusion models, GAN, NeRF , Gaussian splatting etc. )
- Experience with large-scale data pipelines, distributed training, and evaluation frameworks
- Strong problem-solving skills, creativity, and ability to work across cross-functional teams
- Competitive salary
- Opportunity to collaborate with and learn from industry-leading professionals in the automotive domain.
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