Machine Learning Engineer

Waabi, founded by AI pioneer and visionary Raquel Urtasun, is an AI company building the next generation of self-driving technology. With a world class team and an innovative approach that unleashes the power of AI to “drive” safely in the real world, Waabi is bringing the promise of self-driving closer to commercialization than ever before. Waabi is backed by best-in-class investors across the technology, logistics and the Canadian innovation ecosystem.
With offices in Toronto, San Francisco, and Dallas, 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...- Contribute and build the self-driving engineering foundations to develop the next generation autonomy software across many machine learning projects.- Collaborate closely with autonomy and algorithm engineers to scale safe self-driving systems using an AI-first approach.- Build standardized frameworks for training and production aligned with industry best practices.- Comprehensively profile model runtime and memory to pinpoint performance bottlenecks.- Identify and evaluate emerging technologies that can be adopted into Waabi’s training and production frameworks, in areas of training and inference optimization (i.e quantization, model compilation and exporting, etc.), distributed training, PyTorch tooling, etc.- Work with AWS Cloud infrastructure to ensure training job stability and efficiency (i.e compute environments, S3 buckets, etc.).
Qualifications:- MS/PhD or Bachelors degree with a minimum of 2 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.- Ability to rapidly prototype with test driven development methodologies.- Experience in deep learning frameworks such as PyTorch.- Experience in distributed machine learning training, model deployment and monitoring.- Experience building software solutions on cloud infrastructure.- 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 optimizing model training and evaluation, familiar with model profiling tools.- Experience in model compilation and exporting, interaction with lower level concepts like TensorRT, cuda kernels.- Experience in Bazel build systems, and integrating third party packages into dev environments.
The US yearly salary range for this role is: $109,000 - $190,000 USD in addition to competitive perks & benefits. Waabi (US) Inc.’s yearly salary ranges are determined based on several factors in accordance with the Company’s compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations.  Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.
Perks/Benefits:Waabi provides a competitive benefits package that includes:- Competitive compensation and equity awards.- Health and Wellness benefits encompassing Medical, Dental and Vision coverage.- Unlimited Vacation.- Flexible hours and Work from Home support.- Daily drinks, snacks and catered meals (when in office).- Regularly scheduled team building activities and social events both on-site, off-site & virtually.- World-class facility that includes a gym, games room (ping pong table, video game consoles, board games,etc), multiple collaborative working spaces and a gorgeous patio!(when in office).- As we grow, this list continues to evolve! 
Waabi is an equal opportunity employer that celebrates diversity and is committed to creating a supportive, inclusive, and accessible environment for all employees. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.