Senior / Staff Perception Engineer

Waabi
Remote US & CanadaPosted 19 March 2026

Job Description

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 Senior Perception Engineer, you will be at the forefront of advancing and deploying perception algorithms for our self-driving vehicles. You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that enable our vehicles to perceive the world around them. We value originality, innovation, and a commitment to rigorous experimental validation and code quality as we strive to bring research ideas into production and push the boundaries of self-driving technology. You will... - Prototype, evaluate, and iterate on perception algorithms, using real-world data and simulations, to improve the accuracy, robustness, and safety of our self-driving technology. This includes working on 3D detection and tracking of traffic agents as well as semantic understanding of various traffic scenes. - Formulate problems and propose pragmatic and long-term solutions based on research insights, leveraging your expertise in deep learning, computer vision, and self-driving. - Support productizing and deploying perception algorithms to our self-driving vehicles, collaborating closely with platform teams to ensure seamless integration of research findings into our self-driving system. - Champion engineering excellence, ensuring high-quality, well structured and tested code. - Collaborate in a multidisciplinary team solving problems related to perception, motion forecasting, planning, traffic modeling and sensor simulation, integrating research findings into our self-driving system and contributing to the development of a unified self-driving platform. - Stay up-to-date with the latest advancements in the field of artificial intelligence, machine learning, computer vision, and self-driving technologies, and apply insights from the literature. - Work with large datasets from various sources as well as Waabi World, our high-fidelity simulator. - Contribute to the publication of research findings in conferences as well as Waabi's blog. Qualifications: - MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Computer Science, Machine Learning and/or similar technical field(s) of study. - Experience working on applied research projects. - Experience driving projects on 3D detection, tracking of traffic agents and/or semantic understanding of various traffic scenes. - Passion for taking research ideas and turning them into practical solutions for real-world applications. - Solid understanding of computing fundamentals, including code efficiency. - Proficient in Python programming with a focus on writing high-quality, well-structured, and tested code. - Comfortable working with large datasets for machine learning applications. - Strong grasp of machine learning and computer vision literature, including current trends and state-of-the-art techniques. - Open-minded and collaborative team player with the willingness to help others. - Experience and excitement in supporting a fast-paced high-growth environment. - Passionate about being part of an organization solving self-driving technologies. Bonus/nice to have: - Experience in shipping machine learning features/models into production. - Previous experience in multimodal sensor fusion.  - Previous experience in self-driving technology or related fields. - Publications in top-tier conferences or j ... (truncated, view full listing at source)
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