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!
Today at Wayve, our model development cycle is composed of multiple complex training phases, each building on the last. As a Staff Machine Learning Engineer (Ops/Release), you are expected to have a deep understanding of each of the training phases, understanding how our models are created from start to finish. You will ultimately be responsible for setting and enforcing the standard of each of the release gates along this journey, ensuring that each training phase is sufficiently validated before the next phase begins.
You'll drive technical excellence across our ML delivery pipelines. You'll review release content to ensure it meets our standards, identify bottlenecks in the process, and partner with platform teams to make sure tooling meets our delivery needs. You'll work with CI/CD teams to adapt workflows and streamline model delivery, and with evaluation teams to keep our methods reliable — spotting gaps and driving new methodology for evaluating our models.
This is a high-trust, high-visibility role: our release process directly protects our model baseline, and a mistake here has real consequences for how the product performs on-road and how it's perceived externally.
Key responsibilities:
In order to set you up for success as a Staff Machine Learning - Ops at Wayve, we’re looking for the following skills and experience.
Essential
Desirable
This is a full-time role based in our office in London. 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.
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.