Optimize Waabi’s distributed machine learning training with profiling, CUDA, and inference technologies across research and production. This Senior or Staff engineering role focuses on creating stable, efficient training frameworks for Waabi’s autonomous transportation technology. You will investigate CPU and GPU performance, assess quantization-aware training, and explore compilation and deployment techniques. Collaboration with researchers and machine learning engineers will guide optimal resource usage and practical adoption through tooling and dashboards. Key Responsibilities:
Drive distributed training toward greater stability and efficiency Comprehensively profile runtime and memory performance Identify technologies for training and inference frameworks Design new CUDA kernels when appropriate Improve dashboards that encourage broad tool adoption Requirements:
Computer Science, Robotics, or similar technical education Minimum four years industry experience with bachelor's degree Strong coding skills across Python, C++, or Rust Deep learning framework experience with PyTorch or Jax Profiling experience with PyTorch Profiler or NVIDIA Nsight Bring distributed training, performance profiling, and framework optimization experience to Waabi’s growing engineering team.
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Staff Distributed Training Optimization Engineer in Toronto employer: Waabi
Waabi is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for Senior / Staff ML Ops Engineers to thrive. With competitive compensation, unlimited vacation, and a commitment to employee wellness, Waabi supports a work-life balance while providing opportunities for professional growth in the rapidly evolving field of autonomous transportation. Located in Toronto, employees benefit from a vibrant tech community and access to cutting-edge resources, all while contributing to meaningful advancements in AI technology.
