Our team designs, builds, and maintains software supporting the introspection of machine learning-based motion planners. We are responsible for developing introspection techniques and providing autonomy software engineers tooling used to understand and debug motion planning behavior.
In this role, you will:
- Lead new initiatives to introspect the output of machine learning motion planning models. You will both utilize existing cutting edge techniques as well as develop novel introspection techniques.
- Design new architecture and implement new tools used to analyze machine learning model behavior.
- Collaborate with engineers on Perception, Prediction, Planning, Machine Learning Model, and Visualization teams to enable development of behavior improvements.
Qualifications
- MS or PhD in Computer Science, Machine Learning, or a related field
- 5+ years of industry experience in Machine Learning–experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluation
- Proficiency in Python and ML libraries (PyTorch, NumPy, Jax)
- Strong understanding of Imitation and Reinforcement Learning concepts and motion planning search techniques
- Familiarity with common learned model introspection techniques
Bonus Qualifications
- Familiarity with autonomous vehicles or robotics
- Solid understanding of LLM concepts
- Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)