Requirements

• Experience shipping AI or ML systems used by real users, with knowledge of production behavior and failure modes.
• Strong skills in Python and SQL for data assembly, building datasets, and troubleshooting failures.
• Deliberate investment in software engineering practices including testing, error handling, CI/CD, observability, and MLOps.
• Hands-on experience across the GenAI stack: LLM APIs, retrieval and vector stores, agents with tool use, structured outputs, and cloud infrastructure (AWS and GCP).
• A clear perspective on evaluation methods due to experience with system failures.
• Ability to reason about model failures, including hallucination and prompt injection impacts.
• Good judgment on when to use complex models versus simpler solutions.
• Constructive code reviewer who positively influences team quality.

Nice to haves

• Curiosity about underlying mechanisms and persistence beyond first plausible answers.
• Attracted to complex problems without obvious solutions.
• Honesty about knowledge limits and early collaboration with team members.
• Ownership mentality: responsibility for shipped work, monitoring, and fixing issues.
• Appreciation for foundational work that ensures AI feature reliability over mere demos.
• Ability to engage in healthy disagreements and commit to final decisions.

What you'll be doing

• Owning AI features end-to-end from ambiguous problems to reliable, observable systems used by customers.
• Building retrieval and data foundations, treating retrieval quality as a core engineering challenge.
• Designing evaluation and monitoring systems to measure quality, reliability, and safety effectively.
• Developing AI applications and agents using LLMs, orchestration, APIs, and data systems with solid production software practices.
• Making model, cost, and latency decisions affecting operational expenses, with clear documentation.
• Rapidly prototyping, identifying reusable components, and embedding successful patterns team-wide.
• Raising overall work quality through code reviews, design feedback, and setting standards by example.
• Communicating technical decisions directly with product managers and stakeholders, translating model behavior into business impact.

Perks and benefits

• Competitive salary.
• Equity scheme to share in Motorway's success.
• 25 days holiday.
• Flexible working with 2 days a week in the London office, including socials and snacks.
• Private medical insurance via BUPA.
• Life assurance.
• Pension scheme with 5% company 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.