You will:

Own tasks in the ML Driver, take responsibility for task scaling and task performance, create ML methods and recipes to scale and improve tasks.
Analyze behavior of ML systems in real-world application, identify issues and root causes, advise or develop short- and long-term solutions.
Monitor ML systems in production, develop methods for automatically detecting issues or regressions, develop AI-aided analysis and debugging tooling.
Develop and maintain metrics for ADV relevant issues, including safety-critical and longtail issues.

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You have:

Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
3+ years experience in Machine Learning and/or Computer Vision
Experience with Python
Experience with ML frameworks like PyTorch or JAX

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We prefer:

MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI
Experience with C++

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About the company

Waymo

Company overview

Waymo is an autonomous driving company that builds the Waymo Driver and operates commercial robotaxi services under Waymo One. Waymo focuses on deploying fully autonomous ride-hailing in selected cities while continuing to develop perception, planning, simulation, and fleet operations systems. Waymo sits within Alphabet and works across software, hardware, and operations to run real-world driverless fleets. Waymo also publishes regular updates on service expansion and safety and technology progress.

Locations and presence

Waymo is headquartered in Mountain View, with major U.S. presence tied to engineering hubs and fleet operations, and active rider service in places like the San Francisco Bay Area, Los Angeles, Phoenix, Austin, and Atlanta. Waymo also advertises a hybrid work model with some remote opportunities depending on role and team.

Palpable Score

68.8

/ 100

Waymo scores best on pay competitiveness and the presence of paid internships that look like real work rather than shadowing, with public materials describing intern projects and community programming. The biggest constraint is direct entry-level access into full-time roles, which is less visible than internships, plus mixed public signals on internship conversion timing and certainty during periods of organizational change.

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