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Wayve is building an AI platform for autonomous driving using end-to-end learned systems. The Robotaxi team is deploying the AI Driver at public scale with fare-paying riders on global ride-hail platforms.
In this role, you will own and evolve the autonomy performance measurement framework for Robotaxi, defining metrics, dashboards, and decision-making narratives that leadership uses to determine ship/no-ship readiness. You'll build and run the end-to-end performance improvement loop: detecting issues, triaging and prioritizing them, driving root-cause investigations, and landing fixes across the model, robot software, backend, and evaluation tooling.
You'll lead cross-functional execution across AV core, evaluation/validation, robotics, robot software, triage/systems, and SRE teams, aligning roadmaps to performance outcomes without direct reporting authority. You'll define and enforce performance release gates and regression detection for production deployments, owning readiness calls and incident follow-through.
Key responsibilities include standing up Robotaxi's performance operating system from scratch (metrics, triage funnel, release gates, rollback plans); creating external-facing performance reporting for partners like Uber; integrating new model features into live operations; and working with the stack (WayveCode, Python/C++, Looker, Databricks/SQL, Grafana, internal eval/sim tooling). In your first 90 days, you'll map prioritized fixes across backend, robot, and driving model, and ship a new driving model deployment and robot software release end-to-end.
You'll stay hands-on in data and code when needed to accelerate debugging and unblock delivery. Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.
REQUIREMENTS:
- Demonstrated cross-functional engineering leadership, steering complex delivery across multiple teams and stakeholders to production outcomes without direct line management
- Strong product and customer orientation; ability to connect technical performance metrics to rider experience, operational outcomes, and launch readiness
- Strong autonomy/robotics performance debugging experience with deep hands-on work on real-world issues and time-series/real-time signals
- Strong technical foundations in learned systems/ML-driven robotics; ability to reason about model behavior, failure modes, and measurement pitfalls in data-driven autonomy stacks
- Production fluency in Python, C++, and SQL; comfortable working directly in code and analytics pipelines
- Experience owning or co-owning releases to a fleet of robots/vehicles in production, including release processes, gates, rollback plans, and regression handling
- Familiarity with modern data/observability stacks used in autonomy programs (e.g., Databricks/SQL, Grafana, internal eval/sim tooling)
Note: The posting encourages applications from those passionate about autonomy even if not meeting every requirement.
