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Wayve is building embodied AI for autonomous vehicles and robotics. You'll join the Multi-Embodiment Generalist Agent (MEGA) team as a founding member, designing and building the software systems that enable foundation model research for general-purpose robots.
In this role, you'll own key parts of the ML infrastructure and software stack that underpin the research program. Your day-to-day work includes:
- Designing and maintaining scalable pipelines for data ingestion, model training, evaluation, and experimentation across large-scale video and robotics datasets
- Building reusable interfaces and shared tooling that connect data, models, training, evaluation, and downstream robotics workflows
- Partnering closely with researchers to translate evolving model and experiment needs into practical software systems
- Diagnosing and resolving performance, reliability, and usability bottlenecks across large-scale ML workloads
- Raising codebase quality through strong architecture, testing, documentation, and engineering standards
- Supporting distributed training and data processing across multimodal datasets
You'll work with modern ML frameworks, large models, and increasingly complex research workflows. The role sits close to both the models and the data, requiring you to build systems that remain reliable and maintainable as the research program scales. You'll be a core member of the MEGA team, helping shape how the team develops and operates its ML stack.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company is not fully structured—much of how it works is still being written, and you'll help write it. This suits people who want real ownership and can thrive in a fast-moving, research-driven environment.
REQUIREMENTS:
- Strong software engineering skills and experience building high-quality, maintainable software
- Experience building and maintaining machine learning pipelines or infrastructure (data ingestion, training, evaluation, experiment workflows)
- Experience designing software systems and abstractions that are reliable, reusable, and evolve as requirements change
- Hands-on experience with modern ML frameworks and understanding of how ML training and experimentation workflows operate
- Strong debugging skills and ability to investigate problems across complex ML systems
- Experience with software testing, code quality, and engineering practices for maintaining a healthy shared codebase
- Experience working with large datasets, large models, or computationally demanding ML workloads
- Ability to collaborate closely with researchers and engineers and translate research requirements into practical software systems
Note: The posting encourages applications even if you don't meet every requirement, particularly if you're passionate about autonomy and keen to learn.
