Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai You will... Drive Technical Execution: Lead the end-to-end systems engineering lifecycle for the Sensing, Perception, Maps, and Localization domains, ensuring timely delivery of robust solutions across hardware and software teams. Architect Requirements: Create and refine Autonomous Vehicle (AV) Requirements using analytical and numerical methods, focusing on sensing capabilities, perception performance, sensor fusion, mapping, and pose estimation. Own the Contract: Own the definition and requirements development of the Sensing, - - - Perception, and Localization contracts, covering sensor performance, calibration, data quality, and system degradation strategies. Lead Cross-Functional Integration: Act as the primary technical liaison connecting AI and software engineers, hardware engineers, and safety teams to resolve complex system-level bottlenecks and build consensus on architectural trade-offs. Validate via Data: Create methods and use physical and simulated data to inform and verify principled requirements for the entire sensor suite and localization pipeline. Advance Simulation: Improve the simulation and synthetic data generation of sensor models for AVs. Build the Safety Case: Directly contribute to the system safety case by mapping high-level safety goals to deployed architecture, ensuring traceability from requirements through to verification and validation (V&V). Elevate the Team: Mentor junior and mid-level engineers, fostering a culture of rigorous systems engineering, proactive risk management, and flawless execution within the Platform team. Qualifications: Experience: 3+ years of industry experience in autonomous vehicles, robotics, or aerospace, with a proven track record of taking complex autonomous systems from R&D to deployment. Domain Expertise: Strong foundation in AV systems, specifically in sensing (Lidar, Radar, Camera), perception algorithms, sensor fusion, mapping, and localization (e.g., GNSS/INS, SLAM, odometry). Technical Breadth: Deep knowledge of sensor physics, signal processing, data acquisition/management, and dealing with correlated system errors. Systems Rigor: Expert knowledge of Systems Engineering and Verification & Validation (V&V) best practices, including requirements decomposition from high-level safety goals into detailed, verifiable functional and technical specs for Level 4 AV systems. Safety Standards: Working knowledge of functional safety standards (e.g., ISO 26262) and translating probabilistic safety goals into physical, quantifiable metrics. Programming: Comprehensive coding skills in Python, C++, or other common programming languages used for data analysis and systems modeling. Simulation & Modeling: Extensive experience in sensor simulation, error modeling, and perception/localization requirements development. Communication: Exceptional communication and stakeholder management skills, with the ability to distill complex technical risks into actionable decisions for leadership. Bonus: AI/ML Integration: Experience working alongside generative AI architectures or integrating ML-based perception algorithms into deterministic, verifiable safety cases. Advanced Safety: Familiarity with SOTIF (ISO 21448) and handling unknown-unsafe scenarios in autonomous driving. End-to-End Systems: Applied and proven experience in building end-to-end perception or localization systems (e.g., object detection, tracking, scene understanding, HD map integration). Calibration Operations: Experience with sensor calibration and synchronization methods specifically for commercial trucking or heavy machinery. Testing at Scale: Applied and proven experience with defining and executing test cases for perception/localization system performance (e.g., edge cases, challenging weather, GPS-denied environments). Domain Passion: A strong passion for robotics systems and an intuitive understanding of actual electro-mechanical problems in the context of self-driving or robotics. Tooling: Coding in Rust and using build and test automation tools.