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... Be part of a team of multidisciplinary Research Scientists and Engineers working on building a cutting-edge offline perception and auto-labelling system leveraging computer vision, and machine learning. Manage the end-to-end orchestration of the large-scale auto-labelling training, evaluation and automation eco-system. Architect and scale the pipeline to handle large-scale data and user requests using distributed computing frameworks. Collaborate with ML researchers and engineers to seamlessly deploy new architectures into the production environment. Qualifications: Bachelors degree with a Computer Science, Robotics and/or similar technical field(s) of study. 3+ years of experience developing solutions in ML systems or the ML software stack. Deep understanding of ML system architecture, performance analysis, and profiling tools to optimize complex workloads. Experience with the end-to-end productionization of deep learning models, particularly large-scale online inference. Proficient in Python with a track record of writing high-quality, well-structured, and well-tested "production-grade" code. Open-minded and collaborative team player with the willingness to help others. Bonus/nice to have: Familiarity with 3D data (LIDAR/Point Clouds) and multi-modal sensor fusion perception models. Experience working with large Vision-Language Models (VLMs)