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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The Forensics team sits within Model Integration and Release and investigates systemic issues across the full autonomous-vehicle stack—hardware, software, and AI models—to improve vehicle safety and performance.
In this role, you will lead investigations arising from triage escalations, model alerts, on-road incidents, and ad-hoc requests, driving each problem through to root cause. You'll analyze on-road data, logs, and offline simulations to reproduce issues, identify fleet-wide patterns, and test hypotheses. You'll build queries, plots, dashboards, and automated workflows that accelerate investigations and monitor known issues.
You'll work closely with hardware and software engineers, roboticists, model developers, platform teams, operations, and safety specialists. As part of a newly re-formed team, you'll help shape investigative processes, playbooks, and tooling as Wayve's technology and global fleet grow. You'll communicate findings clearly to technical and non-technical stakeholders through written reports, visualizations, and presentations, and support release testing and new vehicle platform bring-up when needed.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with remote flexibility.
REQUIREMENTS:
- At least three years of experience working with complex systems in robotics, autonomous vehicles, or comparable engineering environments
- Curious, pragmatic systems thinker who enjoys hands-on work with ambiguous technical problems and following evidence to root cause
- Ability to investigate across a broad technical stack spanning hardware, software, data, and models
- Experience designing real-world experiments to prove or disprove hypotheses
- Proficiency with Python for data analysis and ability to write scripts for analytical use cases
- Strong communication skills for conveying complex technical findings to both technical and non-technical audiences
- Effective cross-team collaboration with demonstrated ability to influence technical decisions or organizational direction
- Safety-conscious mindset and comfort identifying, escalating, and investigating issues with safety implications
- Valuable but not essential: experience with machine-learning models, safety-critical systems, autonomous vehicles, robotics, automotive engineering, or fast-moving technology companies
