Software Engineer, Applied Machine Learning, Planner Technology

Investigate driving behavior issues from logs and determine root causes in the autonomous driving planner domain.
Translate observed deficiencies into precise learning objectives and design robust evaluation metrics for planner ML models.
Improve the performance of state-of-the-art ML models used by the planner and contribute to scalable ML frameworks.
Develop foundational frameworks that advance the autonomous driver system's capabilities while preserving interpretability and behavioral guarantees.
Expand the impact of ML models across challenging long-tail driving scenarios, ensuring safety and reliability.
Collaborate with software engineers and ML researchers in a hybrid, production-focused role to deliver robust, production-ready solutions.