Requirements

• Experience setting technical direction adopted by engineering teams, with insight into successes and failures.
• Strong skills in Python and SQL, with practical data work experience.
• Deep, current expertise in the GenAI stack, including retrieval at scale, vector stores, agentic orchestration, multimodal work, evaluation infrastructure, and selective fine-tuning.
• Fluency in underlying layers such as MLOps tooling, deployment, and cloud infrastructure (AWS and GCP).
• Experience designing resilient production systems and handling issues when systems fail.
• Strong perspective on evaluation methods distinguishing regression detection from comfort providing.
• High-quality direct contributions that set a standard for peers.
• Ability to explain complex technical trade-offs clearly to non-technical stakeholders.
• Self-awareness about knowledge limits and the instinct to seek expertise early.

Nice to haves

• Preference for simple, reliable systems over overly complex ones.
• Use of authority sparingly, acting as a gatekeeper for poor decisions without being a blocker.
• Appreciation for unglamorous but essential work that ensures AI feature quality beyond demos.
• Enjoyment in mentoring and improving other engineers as much as building personally.
• Proven ability to level up peers through talks, reviews, or day-to-day work.
• Ability to maintain a two-to-three year vision while staying grounded in short-term deliverables.

What you'll be doing

• Setting architecture standards for complex GenAI work by building exemplary solutions.
• Holding sign-off responsibility on complex AI systems to catch architectural, safety, and reliability issues before production.
• Owning shared foundations like retrieval patterns, evaluation infrastructure, data pipelines, and tooling to promote good practices.
• Building the most challenging components and acting as the technical escalation point for difficult problems.
• Designing systems for reliability, affordability, graceful degradation, fallback behavior, and production-scale cost and latency.
• Developing senior engineers by assigning challenging problems, encouraging ownership, and providing candid feedback.
• Shaping hiring and review processes with clear, defensible views of quality standards.
• Providing actionable industry insights for architectural planning.

Perks and benefits

• Competitive salary.
• Equity scheme to share in Motorway's success.
• 25 days holiday.
• Flexible working policy (2 days a week in London office with socials and snacks).
• Private medical insurance via BUPA.
• Life assurance.
• Pension scheme with 5% employer contribution.
• 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.
• One paid volunteering day per year.