Principal 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 lead research on robot foundation models, including model architectures, data strategies, and learning approaches. You will build and evaluate models, curate video datasets, use distributed training infrastructure, guide technical decisions, and translate research into real-world robot capabilities.
Requirements
- Deep 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 drive an independent research agenda and collaborate with engineering and robotics teams
- Experience translating research ideas into working systems, experiments, or deployed capabilities
- Strong communication skills
- PhD in a related technical field
- 5+ years of relevant industry experience
- Experience with real robots, robotic learning, simulation, or policy learning
- Experience with large-scale video data and sequential decision-making systems
- Experience mentoring researchers or leading technical workstreams
Responsibilities
- Lead research into 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
- Influence technical decisions on robot policies, data strategy, and model design
- 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