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Waabi, founded by AI researcher Raquel Urtasun, is developing Physical AI technology for autonomous transportation, including commercial autonomous trucks and robotaxis. The company operates offices in Toronto, San Francisco, Dallas, and Pittsburgh and is backed by leading AI, automotive, and logistics partners.
In this Staff-level role, you will lead software quality efforts for the Planner Architecture—the decision-making system that orchestrates autonomous vehicle behavior in dynamic, uncertain traffic scenarios. Your primary focus is building robust fault-tolerance systems, resolving critical performance bottlenecks, and ensuring continuous safe vehicle operation.
Key responsibilities include:
- Designing and developing motion planning software and algorithms that form the core decision-making engine for self-driving vehicles
- Acting as a technical bridge between Motion Planning and Onboard Software teams, driving overall software quality and reliability improvements
- Managing the experimentation pipeline for research and data generation
- Owning software quality across the full lifecycle, from architecture through production deployment
- Working with a multidisciplinary team of engineers and research scientists using an AI-first approach
You will need a strong foundation in software engineering principles (code review, unit testing, scalability, maintainability, architecture), production experience with planning and decision-making approaches (optimal control, optimization, search methods, probabilistic decision-making), and proficiency in Python or C++. A minimum of 6 years of industry experience in Computer Science, Robotics, or related fields is required, along with proven experience owning software on shipped products with reliability requirements. You should be collaborative, able to balance long-term vision with short-term execution, and passionate about solving hard problems in autonomous systems.
