Staff Machine Learning Engineer, AI Evaluation

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. 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! The role This is a founding Staff Machine Learning Engineer role within Wayve’s Evaluation Tools team, focused on building the model introspection capabilities that accelerate how we develop and ship our AI Driver. You’ll design and productionise tools that reveal how our end-to-end driving models make decisions, enabling faster debugging, earlier regression detection, and more confident releases. Working across Autonomy, Science, Simulation, and Measurement, you’ll embed introspection signals directly into triage and evaluation workflows at scale. It’s a rare opportunity to define how we understand and operationalise explainability at the frontier of AV2.0 — with direct impact on development velocity and on-road performance. Key responsibilities: Design and implement model introspection methods (e.g. saliency/attribution, attention visualisation, latent diagnostics) tailored to our end-to-end driving models Productionise and scale introspection tooling, integrating it into evaluation, triage, and root-cause workflows used across the company Partner with Autonomy and Science teams to identify the most informative internal representations and signals for debugging and model comparison Collaborate with Measurement and Simulation to use introspection signals to better predict on-road performance from off-road testing Build intuitive tools and interfaces with full-stack engineers that make complex model behaviour accessible and actionable Rapidly prototype and iterate on new interpretability approaches as model architectures evolve Own the roadmap for introspection capabilities, balancing quick wins with long-term strategic impact About you In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience. Essential Strong hands-on experience with model introspection / interpretability techniques (e.g. attribution methods, attention analysis, feature importance, etc.) Deep ML fundamentals with experience building and training deep learning models in modern frameworks (e.g. PyTorch) Proven ability to productionise research ideas into reliable, scalable tools used by engineering teams Strong software engineering skills — clean, maintainable code, testing, version control, and performance awareness Ability to translate complex model behaviour into clear, actionable insights for cross-functional stakeholders Desirable Experience working on large-scale, multimodal or temporal models (e.g. vision-language, sequence mo ... (truncated, view full listing at source)