Staff Robotics Engineer, AV Core

Wayve; London (KY); On-site; USD 150,000 - 210,000; Full time

Job summary

Wayve is seeking a Staff Robotics Engineer to steer fault detection and fallback for driverless operation within the AV Core organisation. You will shape the technical direction, train robust fault-detection mechanisms, and collaborate with ML, inference, and software engineers to deliver a safe, scalable robotics stack.

You will lead architecture reviews, mentor engineers, and push for rigorous evidence-based decisions to improve overall robot robustness in real-world deployments.

Qualifications

• Robotics experience with real-world deployments.
• Staff-level technical leadership across programmes.
• Strong Python and C++ proficiency for robotics.
• Excellent safety-focused decision making and communication.

Responsibilities

• Set the technical strategy and roadmap for fault detection and fallback in a robotics stack.
• Design and train fault detection mechanisms for robust driverless operation.
• Collaborate across machine learning, software, and robotics teams.
• Lead integration into the shared driving stack and guide architecture reviews.
• Improve overall robustness of the robot system beyond fault detection.

Skills

Robotics

Staff leadership

Python

C++

System design

Safety-critical

Communication

Job description

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.

As a Staff Robotics Engineer in Wayve's AV Core organisation, you will lead the technical direction and delivery of fault detection and fallback systems for driverless operation. You will work alongside machine learning experts to build robust systems, and do some ML yourself. Physical AI is not just a learning problem; deployment in the real-world requires deep systems understanding of robotics.

The Core Model Safety team builds foundational capabilities for assisted and automated driving – collision avoidance, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering.

Key responsibilities

• Set the technical strategy and roadmap for fault detection and fallback, from a robotics systems perspective, including its behavioral 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 with machine learning, inference optimisation, software engineers, etc.

• 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).

Essential

• Robotics: Proficiency 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.

• Exceptional technical judgement and communication: able to make safety-relevant trade-offs explicit, define the evidence needed for decisions, and lead without relying on formal authority.

• Experience with Python and C++ for robotics.

Desirable

• Operating robotics in the real world at scale: Proven experience in deploying and maintaining fleets of robots or vehicles under real-world conditions.

• Developing tooling to triage and debug robotic systems: Ability to create and refine data collection and analysis tools.

• Cloud infrastructure for monitoring: Experience setting up cloud-based monitoring solutions for large-scale fleets, including dashboards, logging, and real-time alerts.

• Knowledge of embedded / real-time systems: Familiarity with low-level hardware interactions and real-time constraints for safety-critical applications.

• Experience with machine learning and inference optimisation.

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.