Waymo
Software Engineer, Machine Learning, Planner Selection
About this role
Waymo seeks an ML Software Engineer to develop and optimize machine learning models for the Planner Selection system, which makes real-time autonomous driving decisions in complex urban environments. You'll investigate driving behavior issues, improve state-of-the-art ML performance, and build foundational frameworks while maintaining interpretability and safety guarantees.
What you'll do
- Investigate driving behavior issues from logs to identify root causes
- Improve performance of ML models used in the Planner system
- Develop foundational frameworks advancing the Waymo Driver's capabilities
- Expand ML model impact while preserving interpretability and behavioral guarantees
- Address long-tail challenging driving scenarios
- Report to a Technical Lead Manager of Planner Technology
What they're looking for
- Machine Learning model development and optimization
- C++ or Python programming
- Production ML systems experience
- Autonomous vehicle or robotics domain knowledge
- Complex systems engineering
- Data analysis and debugging
- Model interpretability and safety constraints
- Real-time decision-making systems
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous company benefits program
- Work on cutting-edge autonomous driving technology
- Collaborative environment with experienced teams
- Opportunities to solve complex technical challenges
Opens the official application on the employer’s site. No login required.
Waymo
Waymo develops autonomous driving technology and vehicles, building the AI systems, simulation platforms, and infrastructure that power the Waymo Driver. The company is hiring for ML infrastructure engineers, platform engineers, labeling system developers, backend software engineers, and automotive systems engineers to scale its autonomous driving capabilities.
- Website
- waymo.com
Likely interview questions
- Walk us through a time you debugged a production ML model issue—what was your approach and what did you learn?
- Describe your experience optimizing ML model performance in real-time systems. What trade-offs did you consider?