Waymo
Machine Learning Engineer, Sensor Pipelines
About this role
Waymo is hiring a Machine Learning Engineer for the Sensor Pipelines team to develop multi-modal sensor fusion and ML models that enhance the Waymo Driver's perception capabilities. You'll work on real-world autonomous driving challenges using cutting-edge techniques including generative AI, with access to millions of miles of diverse sensor data.
What you'll do
- Build multi-modal sensor fusion architectures and spatial-temporal representation learners using ML techniques
- Develop and deploy machine learning models, including generative AI systems, to solve autonomous driving perception challenges
- Create and maintain data mining, labeling, training, and evaluation pipelines for onboard systems
- Address critical perception problems such as collision detection, antagonistic behavior identification, and occlusion sensing
- Collaborate with product, infrastructure, and research teams across Waymo and Alphabet
- Conduct research on sensor fusion approaches to real-world autonomous vehicle problems
What they're looking for
- Machine Learning and Computer Vision
- Sensor fusion techniques
- Python and C++
- PyTorch or JAX
- Generative AI / Large Language Models
- Data pipeline development
- Multi-modal model architecture design
- Spatial-temporal representation learning
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous company benefits
- Hybrid work schedule
- Access to millions of miles of real-world driving data
- Collaboration with Alphabet research teams
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 with multi-modal sensor fusion—what sensors have you worked with and what architectures did you implement?
- Walk us through a machine learning project where you developed end-to-end pipelines from data labeling to model deployment.