Software Engineer, Applied Machine Learning, Planner Technology

Investigate driving behavior issues from logs and identify root causes.
Translate deficiencies into crisp learning objectives and design evaluation metrics for planner ML models.
Improve the performance of state-of-the-art ML models used by the planner.
Develop foundational frameworks to accelerate advancement of autonomous driving planner capabilities.
Expand ML model impact while preserving interpretability and behavioral guarantees.
Tackle challenging long-tail driving scenarios and contribute to solving complex planning problems.