Staff Machine Learning Software Engineer

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

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.

• 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.

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