Waymo
2027 Summer Intern, PhD, Software Engineer, Simulation
About this role
Waymo is seeking a PhD-level software engineering intern to develop uncertainty-aware algorithms for transformer-based multi-task classifiers used in autonomous driving simulation. You'll implement end-to-end solutions, validate data and models against experiment plans, and contribute directly to improving the Waymo Driver's perception and decision-making capabilities.
What you'll do
- Implement uncertainty-aware algorithms to enhance transformer-based multi-task classifier performance
- Execute end-to-end implementation including model evaluation and validation
- Verify data and model implementations align with experimental specifications
- Debug transformer models using TensorBoard metrics and other diagnostic tools
- Apply data-driven improvements to enhance model performance
- Collaborate with software and hardware engineers on autonomous driving challenges
What they're looking for
- Python programming
- Deep learning frameworks (TensorFlow, JAX)
- Transformer model architecture and debugging
- Machine learning fundamentals
- Uncertainty quantification in deep learning
- Data analysis and model evaluation
- Version control and software development practices
- Robotics or autonomous systems knowledge
Benefits
- Competitive hourly compensation with housing/relocation bonus
- Medical, dental, and vision insurance
- Free meals (breakfast, lunch, dinner, snacks)
- Free Google shuttle access
- Onsite gym facilities
- Intern networking events and professional development
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
- Walk us through your experience debugging transformer-based models—what metrics and tools have you found most useful?
- Describe a project where you used data analysis to improve model performance rather than just tweaking architecture. What was your approach?