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 Teams

Wayve ships a new driving model baseline every week. The Model Integration & Release team runs simulation and on-road testing to decide whether model candidates are ready to promote. As our release process matures, we have an opportunity to learn more from the data we already collect: strengthening how we measure performance, turning on-road findings into better simulation tests, and aligning what we measure with what operators experience in the vehicle.

As a Senior Data Scientist on the Model Integration & Release team, you will own this analytical layer. You will turn evaluation outputs into findings that improve both our models and how we measure them, working across simulation, on-road experiment data, and internal evaluation tooling. You will partner with Validation, Data Science, and Product to improve how we evaluate and make release decisions more confident.

Your day-to-day

You will lead analysis of model and autonomous-vehicle performance data, translating complex signals from simulations, offline evaluation, and on-road testing into clear recommendations for engineering teams. You will investigate performance issues, apply statistical rigour to evaluation data, and identify where our metrics, test coverage, or scoring logic could be improved.

You will work autonomously in a fast-moving weekly release cadence, prioritising analysis work, partnering across teams, and turning investigations into durable improvements.

What you'll be working on:

• Shape how we learn from evaluation data by defining what deeper analysis means in practice and building the habits, tooling, and workflows that support it.
• Improve how we measure driving performance by identifying blind spots, inconsistencies, and gaps in simulation and offline metrics.
• Investigate on-road release-test findings, determine whether offline tests should have caught them, and improve evaluation coverage and measurement.
• Perform experiment analysis and apply statistical methods to provide confident, data-driven promotion recommendations for new driving models.
• Expand what "good" means beyond intervention rates, using the behavioural and operational signals we already collect.
• Partner with Data Science and Validation on how evaluation methods and ground truth are defined, implemented, and maintained.
• Support partner-facing quality investigations by quantifying and tracking issues raised through OEM QA workflows.
• Document findings and recommendations in a way the team can act on within a weekly release cadence, and follow through so the same class of issue does not recur.

You should apply if:

• You have strong analytical and investigative skills and can move from a vague "something looks off" to a clear, evidence-backed conclusion.
• You have hands-on experience working with ML evaluation data, driving-performance metrics, or complex test results at scale.
• You have experience applying statistical methods for A/B testing and experimentation, including confidence intervals, distributions, and sampling.
• You are proficient in Python or SQL and comfortable querying and analysing large datasets.
• You can operate with autonomy in a fast-moving environment: setting priorities within a broader goal and knowing when to solve an issue directly versus when to escalate or partner.
• You communicate clearly in writing and can turn messy investigations into concise, actionable recommendations for technical and non-technical audiences.
• You are genuinely curious about why a model behaves as it does and whether we are measuring the right things.

Experience in autonomous driving, robotics, safety-critical ML systems, simulation-based testing, on-road experiment analysis, OEM or partner validation workflows, or improving evaluation infrastructure and measurement methodology would be beneficial.

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:

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 interviews (Hiring Manager interview and SQL or Python programming interview) 45/60 min
• Deep-dive technical interviews (Quant Domain interview and Metrics Design) 60/60 min
• Final leadership interview (30 mins)

We'll always explain the format and work around your availability.

What's in it for you:

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
• Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs
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.