Waymo
2027 Summer Intern, PhD, Machine Learning Research
About this role
Waymo seeks a PhD-level machine learning research intern to develop and evaluate generative models for autonomous vehicle planning and prediction using JAX. You'll conduct ML experiments, write production-quality code, and collaborate with research and engineering teams on state-of-the-art solutions that transform driving data into robust neural networks.
What you'll do
- Research, implement, and evaluate generative models for planning and predictions using JAX
- Conduct machine learning experiments and evaluate model performance
- Produce high-quality, thoroughly tested code for production use
- Collaborate with research, product, and engineering teams to integrate new concepts
- Debug and analyze complex ML systems and model behavior
- Contribute to autonomous driving perception and decision-making systems
What they're looking for
- Deep learning and reinforcement learning
- JAX, TensorFlow, or PyTorch
- Python and C/C++ programming
- Analytical and debugging skills
- Machine learning experimentation and evaluation
- Neural network design and optimization
- Software testing and code quality practices
- Collaborative research communication
Benefits
- Competitive compensation with housing/relocation bonus if applicable
- Medical, dental, and vision insurance
- Free meals (breakfast, lunch, dinner, snacks)
- Free Google shuttle access
- Onsite gym facilities
- Networking and intern events
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 a recent deep learning project where you implemented and evaluated a model—what was your approach and what did you learn?
- How have you used JAX or similar frameworks to optimize neural networks, and what advantages did you find?