Senior / Staff ML Onboard Optimization Engineer

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... - Collaborate closely with autonomy and algorithm engineers to scale safe self-driving systems using an AI-first approach. - Expand the model deployment pipeline to new GPUs and embedded systems for the next generation of our onboard compute system. - Use frameworks such as TensorRT and modelopt to optimize the models running on the truck. - Create and benchmark new CUDA kernels for inference. - Comprehensively profile model runtime and memory to pinpoint performance bottlenecks.   Qualifications: - MS/PhD or Bachelors degree with a minimum of 6 years of industry experience in Computer Science, Robotics and/or similar technical field(s) of study. - Solid coding proficiency in a variety of coding languages including Python, C++ or Rust. - Experience in deep learning frameworks such as PyTorch. - Skilled in profiling CPU and GPU code using tools such as PyTorch Profiler and NVIDIA Nsight. - Experience with Nvidia embedded platforms such as Nvidia Jetson or Thor. - Open-minded and collaborative team player with willingness to help others. - Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.   Bonus/nice to have: - Experience in model compilation and exporting, interaction with lower level concepts like TensorRT. - Experience in identifying when custom CUDA kernels are needed, and implementing them. - Experience in Bazel build systems, and integrating third party packages into dev environments.