Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our Core Model Safety Team

The Core Model Safety team builds foundational capabilities for assisted and automated driving, including collision avoidance, model understanding, and robustness under failure. It's a small, high-impact team with strong ownership, access to large-scale training and fleet data, and close partners across research, simulation, evaluation, and applied engineering. The work here is central to making driverless autonomy possible: detecting and preventing collisions, developing emergency-driving behaviours from limited data, and making sure the vehicle responds safely when something goes wrong.

🧠 Your day-to-day

Set the technical strategy and roadmap for fault detection and fallback, including its behavioural scope, operating envelope, system interfaces, and measurable acceptance criteria

Design and train fault detection mechanisms using the methods best supported by evidence, to enable robust driverless operation

Collaborate across functions and expertise areas, including machine learning, inference optimisation, and software engineering

Lead integration into the shared driving stack, align technical decisions across teams, and raise the bar through architecture reviews, mentoring, and clear communication of risks, trade-offs, and evidence

Use your judgement and expertise to improve the overall robustness of the robot system, beyond just fault detection and fallback

🧩 What you'll be working on

Fault detection and fallback systems that let the vehicle respond safely when something goes wrong, from strategy through to deployment on the car

Collision avoidance, model understanding, and robustness under failure: the foundational capabilities this team owns for assisted and automated driving

Fault detection methods chosen on the strength of evidence, built to hold up under real driverless operation

Integration into the shared driving stack, working closely with machine learning, inference optimisation, and software engineering partners

Large-scale training and fleet data, with close partners in research, simulation, evaluation, and applied engineering

👤 You should apply if

Proficient in developing, implementing, and troubleshooting robotics solutions, backed by practical, real-world experience

A track record of staff-level technical leadership: setting direction for ambiguous programmes, aligning multiple teams, and carrying work from research through production deployment

Strong technical judgement and communication, able to make safety-relevant trade-offs explicit and lead without relying on formal authority

Comfortable working in Python and C++ for robotics

Experience deploying and maintaining fleets of robots or vehicles at scale under real-world conditions

Bonus if you've built tooling to triage and debug robotic systems, or set up cloud-based monitoring for large-scale fleets

Familiarity with embedded or real-time systems, or hands-on machine learning and inference optimisation experience, is a plus

🌱 Not ticking every box? That's totally okay! If you're passionate about autonomy and keen to learn, we encourage you to apply even if you don't meet every requirement.

More about Wayve

🚗 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.