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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 with teams across the business—hardware and software engineers, roboticists, model developers, platform teams, operations, and safety specialists—to confirm root causes, validate fixes, and stay engaged through resolution.
You'll communicate findings clearly to technical and non-technical stakeholders through written reports, visualizations, and presentations. You'll support release testing and vehicle platform bring-up when investigative or systems-engineering input is needed. 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.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers relocation support and visa sponsorship where applicable, learning and development budgets, comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and access to wellbeing services.
REQUIREMENTS:
- At least three years of experience working with complex systems in robotics, autonomous vehicles, or comparable engineering environment
- Curious, pragmatic systems thinker who enjoys getting hands-on 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
- Confident using Python for data analysis; ability to write scripts for analytical use cases
- Strong communication of complex technical findings to both technical and non-technical audiences
- Effective collaboration across teams; demonstrated ability to use findings to influence technical decisions or organizational direction
- Safety-conscious mindset; comfortable identifying, escalating, and investigating issues with potential safety implications
- Valuable but not essential: experience with machine-learning models, safety-critical systems, autonomous vehicles, robotics, automotive engineering, or fast-moving technology companies
