Senior Applied AI/Machine Learning Scientist, Faire

• 5+ years of industry experience building and shipping production ML/AI systems with measurable business impact — including hands-on ownership of the applied-science side (data, evaluation, modeling, quality), not just system plumbing
• 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
• Strong applied ML / data science foundation — reasons from data, designs experiments and evals, and has turned proprietary or structured data into product capability
• Track record of shipping fast across multiple stacks (backend, data, and ideally frontend) with quality — not a single-layer specialist; demonstrates cross-stack range
• AI-native in practice: uses AI coding tools and agent workflows as a force multiplier in day-to-day work
• Architectural maturity — can explain design choices that work simply today but won’t need to be thrown away when requirements grow
• Operates with high autonomy and resourcefulness, with good judgment about when to escalate and when to just solve it
• Fluent enough in engineering to make sound architecture calls

Desirable

• 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