Machine Learning Software Engineer

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. Build and maintain infrastructure that supports multiple researchers and ML projects without unnecessarily slowing down iteration. Work closely with researchers to understand the requirements of new models and experiments, and translate those requirements into practical software solutions. Build and use distributed training and data-processing pipelines for large models and large multimodal datasets. You should apply if Strong software engineering skills and experience building high-quality, maintainable software. Experience building and maintaining machine learning pipelines or infrastructure, such as data ingestion, training, evaluation, or experiment workflows. Experience designing software systems and abstractions that are reliable, reusable, and able to evolve as requirements change. Hands‑on experience with modern machine learning frameworks and a good understanding of how ML training and experimentation workflows operate. Strong debugging skills and the 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 other computationally demanding ML workloads. Ability to collaborate closely with researchers and engineers and translate research requirements into practical software systems. Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work Locat #J-18808-Ljbffr