Waymo is looking to improve its autonomous driving technology by enhancing the perception system's ability to understand complex road scenarios, specifically focusing on vulnerable road users, traffic controls, and unstructured environments. This involves developing and deploying advanced ML models to ensure safety and efficiency.
Requirements
- 6+ years of experience in Machine Learning, with a strong focus on computer vision and/or deep learning for perception tasks.
- Deep understanding of state-of-the-art ML techniques for object classification, detection, tracking, pose estimation, and/or action recognition.
- Proficiency in at least one major deep learning framework (e.g., TensorFlow, PyTorch, JAX).
- Experience with multi-modal perception systems (e.g., combining camera, lidar, radar data).
- Familiarity with foundation models and techniques for model adaptation (e.g., few-shot learning, transfer learning, domain adaptation).
- Experience in optimizing ML models for on-device deployment and real-time performance.
- Background in autonomous driving, robotics, or a related safety-critical domain.
Responsibilities
- Lead modeling efforts for your respective domain (VRU, Traffic Control or Scene Semantics), encompassing nominal, longtail and fine-grained understanding tasks.
- Leverage onboard and offboard foundation models to streamline your team’s model stack.
- Bring up the model stack for rapid adaptation to new tasks and data.
- Establish and maintain a sustainable model stack to facilitate Waymo's multi-platform deployment and scaling goals.
Other
- Proven experience leading and mentoring a team of engineers or researchers.
- Publications in top-tier ML/CV conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV).
- Hybrid role
