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Greater London, England, United Kingdom
Hybrid
Motorway is the UK's largest online car-selling platform. We connect private sellers directly with over 8,000 dealers nationwide. Our online platform helps sellers achieve great prices for their cars while giving dealers fast, reliable access to the stock they want for their dealerships. Founded in 2017, our award-winning, technology-led approach has redefined the experience of selling a car. Motorway is backed by some of the world’s leading technology investors, having raised £143 million in Series C funding.
This is a unique opportunity to join a fast-growing scale-up at a crucial phase of growth and help change an industry for the better.
GenAI Engineering sits within Motorway's Data and AI function, alongside Machine Learning and the Machine Learning Data Platform. We own the AI features that shape how buyers and sellers experience the marketplace, from agentic workflows in the customer journey to LLM-powered tooling for our dealer network.
What we build goes into the hands of real sellers and thousands of verified dealers, usually within weeks. When it works, someone sells their car more easily. When it doesn't, we hear about it. That feedback loop is short, and it shapes how we work. We have a track record of successful deployments and a strong reputation as a result.
Over the next two years the function scales from shipping AI features to running a serious GenAI platform.
We're looking for a Lead GenAI Engineer to set the technical standard for AI work across the function, and to build the hardest parts of it themselves.
You'll own the architecture and standards for complex, product-facing GenAI work. You'll hold sign-off on the systems that carry real risk, and you'll be the person a squad comes to when they've hit something genuinely hard. This is a player-coach role: your own code sets the bar others refer back to, and you'll spend as much energy making senior engineers better as you do building.
Most of what makes a GenAI feature good sits around the model rather than in it. The prompting is rarely the hard part. Retrieval quality is, and so is the data feeding it, evaluation you can trust, sensible behaviour when things fail, and getting it live and keeping it there. The standards that matter most here are the ones that make that work repeatable across teams. We're not precious about the route you took to this kind of engineering.
Set the architecture standard for complex GenAI work across the function, not by mandate but by building things others learn from and want to adopt.
Hold sign-off on materially complex AI systems, catching architectural, safety and reliability problems before they reach production.
Own the shared foundations that determine feature quality across teams: retrieval patterns, evaluation infrastructure, data pipelines, and the tooling that makes good practice the easy path.
Build the hardest pieces of work directly, acting as the technical escalation point for the function's toughest problems.
Design for systems that stay up and stay affordable, covering graceful degradation, fallback behaviour, and cost and latency at production scale.
Develop the senior engineers around you by handing them harder problems, room to own decisions, and honest feedback.
Shape how we hire and review, bringing a clear and defensible view of what good looks like.
Bring specific, actionable industry insight into planning, explaining what a development means for our architecture rather than noting that the field moves fast.
You've set technical direction that other engineering teams actually adopted, and you can talk about where that worked and where it didn't.
Strong Python, and SQL you genuinely use. A lot of this work is data work: grounding data, golden datasets, and understanding why something failed.
Deep, current expertise across the GenAI stack, including retrieval at scale, vector stores, agentic orchestration, multimodal work, evaluation infrastructure and fine-tuning where it earns its place.
You're fluent in the layer underneath: MLOps tooling, deployment and cloud infrastructure (we run on AWS and GCP), and you can tell the difference between a choice that matters and one that doesn't.
You design systems that survive contact with production, and you've been on the hook when they didn't.
A strong point of view on evaluation, including the difference between evals that catch regressions and evals that provide comfort.
Your direct contributions set a quality bar for the people around you.
You explain hard technical trade-offs to non-technical stakeholders without either condescending or losing the substance.
Self-awareness about where your knowledge runs out, and the instinct to bring in expertise early.
You build with taste, preferring simple systems that work over clever ones that nearly do.
You use authority sparingly. You're not a blocker, but you'll stop a bad decision from shipping.
You get satisfaction from the unglamorous work that makes AI features actually good, not just the part that demos well.
You enjoy making other engineers better as much as building things yourself.
You've levelled people up in a way they'd recognise, whether through a talk, a review, or how you work day to day.
You hold a two-to-three-year view without losing touch with what's shipping this month.
We've got your back, in life's biggest moments and the everyday ones too.
Competitive salary
Equity scheme - we all share in Motorway's success
25 days holiday
Flexible working (2 days a week in our London office, with socials and snacks galore)
Private medical insurance via BUPA
Life assurance
Pension scheme (we contribute 5%)
Enhanced family leave (e.g. 26 weeks full pay for maternity or adoption)
24/7 Employee Assistance Programme
EV leasing scheme
Cycle to work scheme
Nursery salary sacrifice scheme
1 paid volunteering day a year
