Machine Learning Software Engineer

ML Software EngineerWe 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.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-dayDesign 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 onDesign, 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 ifStrong 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 WayveWayve 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 - Locations & Flexible WorkingOur main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.The Interview ProcessOur process is clear and respectful of your time:Initial call / recruiter screen (30 mins)Competency Interviews (Programming and hiring manager interview; 1.5 hours total)Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total)Final interview : Mission & values alignment (45 mins).What's in it for you (Location dependant):Salaries benchmarked against the market annually Meaningful equity, sharing in the ownership and long term success of Wayve Relocation support and visa sponsorship where applicable Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs Learning and development budgets with support for training, conferences and growth Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and moreA quick, honest note before you apply.Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you'll help write it. That suits people who want real ownership more than people who need a settled structure from day one.If that sounds like the kind of problem you want to spend your time on, we'd really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.