Machine Learning Software Engineer

About the role

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our Science Teams

We are looking for an ML Software Engineer to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is building foundation models for general-purpose robots: models that learn from large-scale video, language, and robot-interaction data, then generalize across tasks and embodiments, including mobile manipulators, dual-arm platforms, and humanoids. Our aim is to build agents that can perceive, reason about, and act reliably in the physical world. You will design and build the software systems that enable MEGA’s machine learning research. This includes scalable ML pipelines, data and training infrastructure, and the shared tools and abstractions that allow researchers to move quickly from an idea to a reliable experiment. The role sits close to the models and data. You will work with large-scale video and robotics datasets, modern ML frameworks, and increasingly large models, building systems that remain reliable and maintainable as the research program grows. You will be a core member of the MEGA team, owning key parts of the software and ML infrastructure that underpin the research program, and will help shape how the team develops and operates its ML stack as the program grows.

🧠 Your day-to-day

- Design and maintain scalable pipelines for data ingestion, model training, evaluation, and experimentation.

- Build reusable interfaces and shared tooling across data, models, training, evaluation, and downstream robotics workflows.

- Partner with researchers to translate evolving model and experiment needs into practical software systems.

- Diagnose and resolve performance, reliability, and usability bottlenecks across large-scale ML workloads.

- Raise the quality of the shared codebase through strong architecture, testing, documentation, and engineering standards.

- Support distributed training and data processing across large video, language, and robot-interaction datasets.

🧩 What you’ll be working on

- Design, build, and maintain scalable ML pipelines for data ingestion, model training, evaluation, and related research workflows.

- Build software systems that allow ML workloads to scale to larger datasets, larger models, and more experiments.

- Develop clean and reusable interfaces between data, models, training, evaluation, and downstream robotics workflows.

- Own and improve the health of the MEGA codebase, including software architecture, testing, reliability, maintainability, and engineering standards.

- Identify and resolve performance, reliability, and usability bottlenecks across ML workflows.