Senior Applied AI/Machine Learning Scientist (Compass)

• Faire is building the future of wholesale, connecting independent retailers with the brands that will define their stores. At the heart of this mission is Compass — Faire’s user facing AI bet within the Discovery Pillar — building an always-present, context-aware retailer assistant along the enagement journey
• Compass helps retailers make smarter buying decisions by combining Faire’s rich proprietary data with agentic AI and web search, and is increasingly able to take action on retailers’ behalf
• As a Senior Applied AI/ML Scientist on the Compass team, you will be the science and technical lead for this product — driving agent quality through data, evaluation, and modeling, while shipping product features end-to-end with high velocity
• This is a deeply hands-on individual contributor role: no direct reports, keyboard first. You will set the data-grounded direction for how the assistant works, while also being a full-stack (AI, ML, backend) builder who turns ideas into shipped product fast
• You will work at the frontier of agentic AI, blending applied science rigor (eval-driven development, experimentation, data strategy) with cross-stack engineering range to build the retailer assistant of the future. This is a rare opportunity to shape a product from near-zero — where your judgment, speed, and instincts will define the outcomes
• Own the science and technical north star for Compass’s agentic products — the retailer assistant today and whatever comes next: how to leverage Faire’s proprietary data, agent + tool + context strategy (preload vs. tool-calling vs. hybrid), and how to measure and raise agent quality as systems gain the ability to act
• Ship retailer-assistant features end-to-end — across the FLARE Python app, data plumbing, tool wrappers, and the frontend surfaces where the assistant appears, using AI-native workflows to multiply your output
• Translate ambiguous product bets into sequenced, de-risked tactical plans — what to build now, what to defer, and which bets carry the highest impact × probability-of-success
• Set and raise the bar for eval- and experiment-driven development — define how the team knows an agent is good, including offline eval suites, LLM-as-judge metrics, and quality criteria per surface and retailer journey
• Make pragmatic engineering choices: simple enough to ship now, designed to evolve — not over-engineered for imagined future scale, but not throwaway either
• Partner closely with engineers on architecture and serving tradeoffs, and act as the science/technical interface to adjacent teams (Search, Personalization, Platform/FLARE)
• Raise the team’s collective judgment through prototypes, analyses, design reviews, and pairing

Benefits

• Comprehensive healthcare: Including Health, Dental, Vision and Disability for all our locations.
• Time off: Paid time off, holidays and company-wide “Faire Fundays”.
• Parental leave: Generous parental and family leave, as well as fertility support benefits.
• Productivity support: Monthly stipends to help cover work from home connectivity needs.
• Annual learning grant: For personal and professional development, as well as unlimited access to training courses through LinkedIn Learning.
• Thoughtfully designed spaces: All of our offices have been designed with local in mind – from our architects to our coffee blends.
• Fitness and well-being benefits: Including monthly credit towards your wellness-related programmes.
• Mental health benefits: Including free access to Modern Health therapists and resources.
• Charitable matching: Faire will match up to £250 of your charity donations, every year.
• Career planning: Whether you want to grow as a leader, hone your craft or explore a new discipline, our career framework allows space for you to explore.- Operates with high autonomy and resourcefulness, with good judgment about when to escalate and when to just solve it
• Architectural maturity — can explain design choices that work simply today but won’t need to be thrown away when requirements grow
• Track record of shipping fast across multiple stacks (backend, data, and ideally frontend) with quality — not a single-layer specialist; demonstrates cross-stack range
• Has shipped agentic / LLM-powered features in a core production product — with a deep, opinionated grasp of agent design tradeoffs: eval strategy, latency/cost/quality tension, tool-calling vs. context preload, guardrails, and failure containment
• Fluent enough in enginee…