Machine Learning Software Engineer

Wayve • Sunnyvale, CA

1d • $311,850 - $419,760 • Hybrid

About The Position

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.

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.

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.
• 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.

Benefits

• 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 more

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed