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 and San Francisco, 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 to the autonomy stack for self-driving initiatives by bringing innovative state-of-the-art deep learning models that will enable safe self-driving at scale.

- Work on a number of end-to-end autonomy engineering tasks from conceptualization, design, implementation to validation as it relates to cutting-edge algorithmic solutions used to make self-driving a reality. This encompasses core areas of autonomy stack (i.e., prediction, perception, motion planning and control).

- Develop in-depth understanding of deep learning models and algorithms and contribute to optimizing their training protocols, test-time performance, runtime, memory footprint, as well as power consumption.

- Identify, propose and build infrastructure, data and efficient pipelines, data storage strategies, common libraries and useful tools needed to improve research and development of deep-learning models.

- Design and implement tools for automated model tuning and hyper-parameter optimization, as well as experiment analysis.

- Research, validate and incorporate emerging machine learning and research infrastructures, tools, and technologies.

Qualifications:

- MS/PhD or Bachelors degree with a minimum of 2 years of industry experience in Computer Science, Machine Learning and/or similar technical field(s) of study.

- Solid knowledge in performance evaluation and optimization of deep learning or computational science algorithms in the areas of GPU kernel development and optimization.

- Experience with parameter and architecture tuning of deep learning algorithms.

- Solid coding knowledge of Python and C++ internals including scientific computing libraries.

- Demonstrable track-record of learning and deep-diving as needed into complex existing and new technologies within autonomy systems (i.e., perception, prediction, motion planning and control).

- Some experience in reading and developing production quality software, versus only creating prototypes/proof of concepts.

- Open-minded and collaborative team player with the willingness to help others.

- Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.

- Experience in TensorFlow or PyTorch.

Bonus/nice to have:

- Experience in CUDA, OpenCL, etc.

- Familiarity with considerations related to sensor data (e.g., RGB, LiDAR) such as calibration, data capturing, noise sources, transformations, etc.

- Experience with automated testing.

- Experience working in an Agile/Scrum environment.

- Experience in one or more of the following: TensorFlow and PyTorch.