<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Faire</strong></span></p> <p>Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.</p> <p>We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.</p></div><p><strong>About the role</strong></p> <p>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 <strong>Compass</strong> — 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.</p> <p>As a <strong>Senior Applied AI/ML Scientist</strong> 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.</p> <p>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.</p> <p><strong>What you’ll do</strong></p> <ul> <li><strong>Own the science and technical north star</strong> 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.</li> <li>Ship retailer-assistant features <strong>end-to-end</strong> — 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.</li> <li>Translate ambiguous product bets into <strong>sequenced, de-risked tactical plans</strong> — what to build now, what to defer, and which bets carry the highest impact × probability-of-success.</li> <li>Set and raise the bar for <strong>eval- and experiment-driven development</strong> — 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.</li> <li>Make <strong>pragmatic engineering choices</strong>: simple enough to ship now, designed to evolve — not over-engineered for imagined future scale, but not throwaway either.</li> <li>Partner closely with engineers on architecture and serving tradeoffs, and act as the science/technical interface to adjacent teams (Search, Personalization, Platform/FLARE).</li> <li>Raise the team’s collective judgment through prototypes, analyses, design reviews, and pairing.</li> </ul> <p><strong>You’re a great fit if you have…</strong></p> <ul> <li><strong>5+ years</strong> of industry experience building and shipping production ML/AI systems with measurable business impact — including <strong>hands-on ownership</strong> of the applied-science side (data, evaluation, modeling, quality), not just system plumbing.</li> <li>Has shipped <strong>agentic / LLM-powered features</strong> 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.</li> <li>Strong <strong>applied ML / data science foundation</strong> — reasons from data, designs experiments and evals, and has turned proprietary or structured data into product capability.</li> <li>Track record of <strong>shipping fast across multiple stacks</strong> (backend, data, and ideally frontend) with quality — not a single-layer specialist; demonstrates cross-stack range.</li> <li><strong>AI-native</strong> in practice: uses AI coding tools and agent workflows as a force multiplier in day-to-day work.</li> <li>Architectural maturity — can explain design choices that work simply today but won’t need to be thrown away when requirements grow.</li> <li>Operates with high autonomy and resourcefulness, with good judgment about when to escalate and when to just solve it.</li> <li>Fluent enough in engineering to make sound architecture calls.</li> </ul> <p><strong>Bonus points for…</strong></p> <ul> <li>E-commerce, marketplace, or two-sided platform context — understanding of both sides of the retailer/brand dynamic.</li> <li>Experience evolving a <strong>read-only assistant into one that takes actions safely</strong> — confirm-first patterns, guardrails, and failure containment.</li> <li>Hands-on experience with the <strong>OpenAI Agents SDK</strong> or similar agentic frameworks in production.</li> <li>Familiarity with <strong>preload-over-RAG context strategies</strong>, Snowflake-backed grounding, or hybrid approaches.</li> <li>Prior