Staff 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 the role

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. A major part of the role will be designing and maintaining robust workflows and reusable infrastructure that can support multiple ML projects. This emphasis on infrastructure, software systems, repo health, testing and maintainability is central to the role.

You will be a core member of the MEGA team, owning key parts of the software and ML infrastructure that underpin the research program. You will work directly on the systems used to build, train, evaluate, and scale our models, and help shape how the team develops and operates its ML stack as the program grows.

Key Responsibilities

• 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.

About You

In order to set you up for success as an ML Software Engineer at Wayve, we're looking for the following skills and experience.

Essential

• 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.

Desirable

• Experience with distributed training, multi-node systems, or large-scale data processing.
• Experience supporting foundation-model training or other large-scale ML research.
• Experience with multimodal models, video models, vision-language models, or related ML systems.
• Experience working with robotics, embodied AI, simulation, or robot-interaction data.
• Experience building infrastructure in a fast-moving applied research environment where requirements evolve rapidly.
• Experience improving the performance, reliability, or developer experience of ML training and experimentation systems.

This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $311,000 to $419,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.

A 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.