Staff Machine Learning Software Engineer
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What you'll do
- 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.
What they're looking for
- 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.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About us
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!
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.
Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
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
For US candidates only, please visit E-Verify Notice and Participation and Right to Work
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.
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