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Motorway is the UK's largest online car-selling platform, connecting private sellers with over 8,000 dealers nationwide. The company has raised £143 million in Series C funding and is scaling rapidly.
The GenAI Engineering function sits within Data and AI, owning AI features that shape the marketplace experience—from agentic workflows in customer journeys to LLM-powered tooling for dealers. The team ships features to real sellers and thousands of verified dealers within weeks, with a short feedback loop and strong track record of successful deployments.
You'll join as Lead GenAI Engineer, setting technical standards for complex, product-facing GenAI work across the function. This is a player-coach role: you'll own architecture and sign-off on systems carrying real risk, serve as the technical escalation point for the hardest problems, and develop senior engineers around you.
Key responsibilities include:
- Setting architecture standards for complex GenAI work by building exemplary systems others adopt
- Holding sign-off on materially complex AI systems, catching architectural, safety, and reliability issues before production
- Owning shared foundations: retrieval patterns, evaluation infrastructure, data pipelines, and tooling that makes good practice the default
- Building the hardest pieces directly and mentoring senior engineers
- Designing systems for production reliability, graceful degradation, and cost/latency optimization
- Shaping hiring and technical review processes
- Bringing actionable industry insight into planning
You'll report to the Engineering Manager for GenAI Engineering, partnering closely with the Principal GenAI Engineer, Director of Data and AI, and product leadership.
Required expertise: strong Python and SQL; deep knowledge of the GenAI stack (retrieval at scale, vector stores, agentic orchestration, multimodal work, evaluation infrastructure, fine-tuning); MLOps tooling and cloud infrastructure (AWS/GCP); production-hardened system design; and ability to explain technical trade-offs to non-technical stakeholders.
Ideal candidates have set technical direction adopted by other teams, build with taste and simplicity, use authority sparingly, find satisfaction in unglamorous work that makes AI features genuinely good, and enjoy leveling up other engineers.
