Machine Learning Software Engineer

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 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. The role involves designing and building the software systems that enable MEGA's machine learning research, including scalable ML pipelines, data and training infrastructure, and shared tools and abstractions. The role sits close to the models and data and involves working with large-scale video and robotics datasets, modern ML frameworks, and increasingly large models.

Responsibilities

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; Build software systems that allow ML workloads to scale to larger datasets, larger models, and more experiments; Own and improve the health of the MEGA codebase, including software architecture, testing, reliability, maintainability, and engineering standards; 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 them into practical software solutions; Build and use distributed training and data-processing pipelines for large models and large multimodal datasets

Requirements

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

Benefits

Salaries benchmarked against the market annually; Meaningful equity; 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 more