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 Engineering TeamsHow do we know when an AI Driver is ready for a fully driverless Robotaxi service? Our Product Architecture & Safety team brings together data, systems thinking and engineering expertise to help answer that question.
Working closely with Model Development, Behavioural Validation, Safety and Robotaxi Engineering, you'll turn real-world driving, validation and operational data into evidence of what the Wayve AI Driver can do today-and what needs to improve next. Your work will help shape development priorities and measure progress towards a safe, reliable and scalable driverless service.
Your day-to-dayYou'll take ownership of hands-on analysis, from exploring complex datasets in Python and SQL to sharing findings that guide product and engineering decisions.
- Investigate driving behaviours and operational events to understand where, why and how often the system falls short of driverless service expectations.
- Build practical metrics, performance baselines and dashboards to track progress across model, software and product releases.
- Apply statistical methods to assess rare events, quantify uncertainty and distinguish meaningful improvements from noise.
- Work with teams across Wayve to turn findings into development priorities, targeted validation and better data collection.
- Contribute to product readiness reviews with clear evidence of progress, remaining limitations and the confidence behind your conclusions.
What you'll be working on:- Understanding driverless readiness: Assess real-world driving and validation data to identify performance gaps, quantify their operational impact and track whether they are closing.
- Separating performance gaps from missing capabilities: Identify where better model performance can help and where additional system or product capabilities are needed.
- Measuring the complete Robotaxi experience: Define metrics for remote assistance, passenger pickup and drop-off, passenger interactions, vehicle readiness and recovery from operational disruptions.
- Evaluating service reliability: Measure intervention demand, mission completion, recovery time and vehicle availability to understand what it takes to deliver a dependable driverless service.
- Exploring challenging scenarios: Investigate safety-critical interventions, unnecessary stops, stuck scenarios and manoeuvre execution across different roads and operating conditions.
You should apply if:- You have at least three years of relevant experience in data science, automotive data analysis or a related field, with deeper experience for senior-level opportunities.
- You're hands-on with Python, SQL, statistical analysis and tools for exploring and visualising large datasets.
- You can turn an ambiguous real-world problem into measurable questions, useful KPIs and a practical analytical approach.
- You've analysed complex systems, uncovered performance limitations and translated your findings into product or engineering improvements.
- You understand statistical confidence, rare-event analysis, performance distributions and trends.
- You're comfortable working independently with incomplete data, making reasonable assumptions and refining your approach as new evidence emerges.
- You communicate clearly with engineering, product and leadership stakeholders, explaining both your findings and their limitations.
Experience in autonomous driving, ADAS, robotics, mobility services or another complex physical system would be valuable. Familiarity with vehicle dynamics, driving behaviour, safety-critical events or operational reliability would also help. A relevant master's degree or PhD in Physics, Statistics or Mathematics, particularly focused on traffic systems, autonomous driving deployment or urban mobility, is a plus.
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.
How we work - Locations & Flexible Working:This role is available on a hybrid basis in Sunnyvale or remotely within the USA.
Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.
The Interview Process:Our process is clear and respectful of your time:
- Initial call / recruiter screen (30 mins)
- Competency interview (hiring manager interview 60 min)
- Deep-dive technical interviews (programming, statistical interview - 2 hours total)
- Final interview (30 min)
We'll always explain the format and work around your availability.
What's in it for you (Location dependent): Salaries benchmarked against the market annually
Meaningful equity, sharing in the ownership and long term success of Wayve
Relocation support and visa sponsorship where applicable
• Flexible working arrangements appropriate to your role and location, with opportunities to collaborate in person
Learning and development budgets with support for training, conferences and growth
Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more
A quick, honest note before you apply.
Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you'll help write it. That suits people who want real ownership more than people who need a settled structure from day one.
If that sounds like the kind of problem you want to spend your time on, we'd really like to hear from you.
