Research Scientist Robot Foundation Model
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will research robot foundation models and learning approaches. You will design, implement, and evaluate models, curate large video datasets, build distributed training pipelines, collaborate on real-world robot performance, and communicate research internally and through publications where appropriate.
Requirements
- Experience in machine learning focused on vision-language models, video models, robot policies, robotics foundation models, or embodied AI
- Experience with scalable multi-node training, large datasets, or large model training
- Research track record with top-tier publications
- Strong coding skills and experience with modern machine learning frameworks
- Ability to design and run rigorous experiments with engineering and robotics teams
- Experience translating research ideas into working systems, experiments, or deployed capabilities
- Strong communication skills
- PhD or MS in a related technical field
- Industry experience in machine learning, robotics, embodied AI, or applied research
- Experience with real robots, robotic learning, simulation, or policy learning
- Experience with large-scale video data and sequential decision-making systems
Responsibilities
- Research model architectures, data, and learning approaches for robot foundation models
- Design, implement, and evaluate robotics foundation-model architectures
- Develop reinforcement learning, behavioural cloning, and robot-policy learning approaches
- Synthesize, curate, and filter large-scale video datasets
- Build and use scalable distributed training pipelines
- Connect research progress to real-world robot performance
- Communicate research internally and contribute to external publications where appropriate
Benefits
- Competitive equity package
- Flexible working hours