About the job
At Wayve, we are dedicated to fostering a diverse, equitable, and respectful culture that values every individual based on their unique skills and perspectives, irrespective of sex, race, religion, ethnicity, disability, age, citizenship, marital status, sexual orientation, gender identity, veteran status, or any other characteristic protected by law.
About Us
Founded in 2017, Wayve is at the forefront of developing Embodied AI technology. Our sophisticated AI software and foundational models empower vehicles to perceive, comprehend, and navigate complex environments, greatly enhancing the safety and usability of automated driving systems.
Our vision is to create autonomy that drives the world forward. Our intelligent, mapless, and hardware-agnostic AI products are tailored for automakers, facilitating the shift from assisted driving to fully automated systems. In our dynamic environment, we thrive on tackling big challenges and embrace the uncertainty that comes with them, continuously striving for innovative solutions. We aim high while remaining humble in our pursuit of excellence, constantly learning and evolving to pave the way for a smarter, safer future.
Your contributions at Wayve will have a significant impact. We celebrate diversity, welcome fresh perspectives, and cultivate an inclusive work environment where we support each other to make a difference.
Join us at Wayve and define your career journey!
The Role
As a pioneering Staff Machine Learning Engineer within Wayve’s Evaluation Tools team, your focus will be on enhancing model introspection capabilities that expedite the development and deployment of our AI Driver. You will design and implement tools that elucidate how our end-to-end driving models make decisions, facilitating quicker debugging, earlier regression detection, and more assured releases. Collaborating across Autonomy, Science, Simulation, and Measurement, you'll integrate introspection signals directly into triage and evaluation workflows at scale. This is a unique opportunity to shape the understanding and application of explainability at the cutting edge of AV2.0, directly influencing development speed and on-road performance.

