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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The Core Model Safety team develops foundational capabilities for assisted and automated driving, including collision avoidance, model understanding, and robustness under failure modes.
In this Staff-level role, you will set technical strategy and roadmap for fault detection and fallback systems that enable safe driverless operation. Your responsibilities include:
- Establishing technical direction and measurable acceptance criteria for fault detection and fallback capabilities, including behavioral scope, operating envelope, and system interfaces
- Designing and training fault detection mechanisms using evidence-based methods to support robust autonomous operation
- Collaborating across machine learning, inference optimization, and software engineering teams
- Leading integration into the shared driving stack, conducting architecture reviews, and mentoring team members
- Raising the bar on overall system robustness through technical leadership and clear communication of risks and trade-offs
- Working with large-scale training and fleet data in partnership with research, simulation, evaluation, and applied engineering teams
You'll own fault detection and fallback systems from strategy through production deployment on vehicles, ensuring the vehicle responds safely when something goes wrong. The work is central to making driverless autonomy possible.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company emphasizes hands-on involvement in vehicle workshops and labs alongside remote flexibility.
**Requirements:**
- Proficiency in developing, implementing, and troubleshooting robotics solutions with practical, real-world experience
- Track record of staff-level technical leadership: setting direction for ambiguous programs, aligning multiple teams, and carrying work from research through production deployment
- Strong technical judgment and communication skills, able to make safety-relevant trade-offs explicit and lead without 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: experience building tooling to triage and debug robotic systems, or setting up cloud-based monitoring for large-scale fleets
- Plus: familiarity with embedded/real-time systems, or hands-on machine learning and inference optimization experience
