Waymo
ML Engineer, Foundation Model Recipes
About this role
Waymo is hiring an ML Engineer to develop advanced recipes for foundation models that improve autonomous driving safety and performance. You'll work on cutting-edge ML solutions including reinforcement learning, generative modeling, and robust evaluation while partnering across teams to deploy innovations in production.
What you'll do
- Develop and extend state-of-the-art ML recipes for embodied AI agents in autonomous driving contexts
- Design and implement large-scale data and evaluation pipelines
- Drive model development through data curation, evaluation, and systems optimization
- Collaborate with internal and external teams to land disruptive technology in production
- Work with modern foundation models and deep learning frameworks
- Contribute research in areas like reinforcement learning, learning from demonstration, and Bayesian inference
What they're looking for
- Python programming
- Deep learning frameworks (PyTorch, JAX, or TensorFlow)
- Machine learning model development and recipes
- Data pipeline design and implementation
- Distributed systems and large-scale data processing
- Robotics and autonomous systems knowledge
- Evaluation metrics and benchmarking
- Software engineering and system design
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous company benefits package
- Hybrid work arrangement
- Multiple location options across US
- Opportunity to work on world-class autonomous driving technology
Opens the official application on the employer’s site. No login required.
Waymo
Waymo develops autonomous driving technology and vehicles, building the AI systems, simulation platforms, and infrastructure that power the Waymo Driver. The company is hiring for ML infrastructure engineers, platform engineers, labeling system developers, backend software engineers, and automotive systems engineers to scale its autonomous driving capabilities.
- Website
- waymo.com
Likely interview questions
- Describe your experience developing ML recipes or training pipelines in an industrial or research setting—what was your specific contribution?
- Walk us through how you would design and implement a large-scale evaluation pipeline for foundation models in autonomous driving.