Senior / Staff Software Engineer, High-Performance Onboard Algorithms

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 As a Software Engineer in High-Performance Onboard Algorithms, you will be a key contributor to the development and optimization of our real-time signal processing pipeline for autonomous driving. You will collaborate with our team of world-renowned scientists and engineers to build innovative, practical, and scalable solutions that handle massive amounts of sensor data (camera, LiDAR, radar, and others) with low latency and high reliability. We value original, high-impact ideas and rigorous experimental validation. You will… - Be part of a multidisciplinary team of Engineers and Scientists working on building a best-in-class, high-performance sensor processing stack. - Design, implement, and optimize highly efficient algorithms for sensor data acquisition, processing, fusion, and filtering, ensuring real-time performance on target hardware. - Work on shipping next-generation signal processing software, leveraging parallel computing architectures (e.g., CPU, GPU, specialized accelerators) and low-level system programming. - Collaborate with Waabi’s autonomy and hardware teams to ensure the efficiency and robustness of the entire system. Qualifications: - Fundamentals of computer architecture. You have a deep understanding of how modern computer systems work (multi-core, vector units, cache management, memory hierarchy, networking). - Data-parallel algorithms. You have experience implementing data-parallel and