Senior / Staff Machine Learning Engineer

Waabi, an innovative AI company at the forefront of self-driving technology, is seeking a Senior/Staff Machine Learning Engineer to join their growing team. Founded by AI pioneer Raquel Urtasun, Waabi is revolutionizing the autonomous vehicle industry with their AI-first approach to self-driving technology.

The role offers an exciting opportunity to work on cutting-edge autonomous systems, combining machine learning expertise with practical engineering challenges. As a Senior/Staff ML Engineer, you'll be instrumental in developing the next generation of autonomy software, working with state-of-the-art deep learning frameworks and distributed systems.

The position demands a strong technical background with at least 6 years of experience, offering a competitive salary range of $158,000 - $269,000 USD, plus equity and performance bonuses. You'll be working with a world-class team across offices in Toronto, San Francisco, and Dallas, with flexible hybrid working arrangements.

Key responsibilities include building standardized frameworks for training and production, optimizing model performance, and scaling safe self-driving systems. The ideal candidate will have extensive experience with Python, deep learning frameworks like PyTorch, and cloud infrastructure, combined with a passion for autonomous technology.

Waabi offers an impressive benefits package including unlimited vacation, comprehensive health coverage, and regular team activities. The company maintains a strong commitment to diversity and inclusion, making it an ideal workplace for innovative and collaborative professionals who want to make a significant impact in the autonomous vehicle industry.

This role presents a unique opportunity to work with cutting-edge technology while contributing to the future of transportation. The company's strong backing from top-tier investors and its innovative approach to self-driving technology make it an exciting time to join Waabi as they push the boundaries of what's possible in autonomous systems.