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 Robotaxi Teams

The Robotaxi team is taking Wayve's AI Driver to its first public-scale deployment, with fare-paying riders on global ride-hail platforms. This team owns the end-to-end performance operating system for the product: the metrics, triage, and release gates that decide whether the autonomy is good enough to ship, and stays great release over release. For an end-to-end learned driving system, there's no existing playbook to follow: this team is defining what "performance" means from scratch.

🧠 Your day-to-day

Own and evolve the autonomy performance measurement framework for Robotaxi (driving + pick-up/drop-off): the metrics, dashboards, and narrative leadership uses to make ship/no-ship decisions

Build and run the end-to-end performance improvement loop: detect issues, triage and prioritise, drive root-cause investigations, and land fixes across model, robot software, backend, and evaluation tooling

Lead cross-functional execution across AV core, evaluation/validation, robotics, robot software, triage/systems, and SRE, aligning roadmaps to performance outcomes without direct reporting authority

Define and enforce performance release gates and regression detection for production deployments, owning readiness calls and incident follow-through

Stay hands-on in the data and code when needed (Python/C++/SQL) to accelerate debugging and unblock delivery

🧩 What you'll be working on

Standing up Robotaxi's performance operating system from scratch: metrics, triage funnel, release gates, and rollback plans for an end-to-end learned driving system with no existing playbook to copy

External-facing performance reporting that's trusted and repeatable enough to share directly with partners like Uber

Integrating new model features and architectures from core engineering into live Robotaxi operations

The stack underneath it all: WayveCode (Python + C++), Looker, Databricks/SQL, Grafana, and internal evaluation and sim tooling

In your first 90 days: mapping the top prioritised fixes across backend, robot, and driving model, and shipping a new driving model deployment and robot software release end-to-end

👤 You should apply if

You've demonstrated cross-functional engineering leadership, steering complex delivery across multiple teams and stakeholders to production outcomes, without relying on direct line management

You bring a strong product and customer orientation, and can connect technical performance metrics to rider experience, operational outcomes, and launch readiness

You have strong autonomy/robotics performance debugging experience, including deep hands-on work with real-world issues and time-series/real-time signals, rather than just managing a queue

You have strong technical foundations in learned systems/ML-driven robotics, and can reason about model behaviour, failure modes, and measurement pitfalls in data-driven autonomy stacks

You're production-fluent in Python, C++, and SQL, comfortable working directly in code and analytics pipelines to diagnose problems and validate improvements

You have experience owning or co-owning releases to a fleet of robots/vehicles in production, including release processes, gates, rollback plans, and regression handling

You're familiar with modern data/observability stacks used in autonomy programs (e.g. Databricks/SQL, Grafana, internal eval/sim tooling)

🌱 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.