Developing synthetic sensor simulation models and algorithms to improve autonomous driving technology.
Requirements
- Deep understanding of ML fundamentals with hands-on experience in training and evaluating modern ML models.
- Strong Python skills with experience in deep learning frameworks, e.g., PyTorch, TensorFlow, or Jax.
- Deep understanding of 3D geometry and state estimation fundamentals.
- Proficiency in systems coding.
- Experience in simulating/modeling real sensors (camera, lidar, radar, IMU, etc...), including noise modeling.
- Experience in modern ML graphics techniques, e.g., NeRF, Gaussian Splatting, and/or generative models.
Responsibilities
- Research, develop, and implement state-of-the-art synthetic sensor simulation methods.
- Analyze and characterize the realism and utility of synthetic sensor data.
- Answer critical questions about sensor data and autonomy performance.
- Collaborate with stakeholders across autonomy, infrastructure, and systems teams on map needs and requirements.
Other
- One of: PhD in machine learning, computer science, electrical engineering, robotics, or related field, and 3+ years of industry experience, Masters and 4+ years of industry experience, or 5+ years of industry experience.
- Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA).
