Senior Applied AI/Machine Learning Scientist (Compass)

Join Faire, a company revolutionizing wholesale by connecting independent retailers with defining brands. As a Senior Applied AI/Machine Learning Scientist, you will lead the development of Compass, an AI-driven retailer assistant. This hands-on role requires expertise in AI, ML, and backend engineering, with a focus on data-driven decision-making and product feature development. Enjoy comprehensive benefits, including healthcare, paid time off, parental leave, and professional development support.

• Lead the development and implementation of agentic AI features for the Compass product, driving agent quality through data, evaluation, and modeling.
• Collaborate with cross-functional teams to translate product requirements into actionable plans, ensuring timely delivery of features.
• Set and raise the bar for evaluation and experiment-driven development, defining how the team measures agent quality and success.
• 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 engineering to make sound architecture calls
• 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
• AI-native in practice: uses AI coding tools and agent workflows as a force multiplier in day-to-day work
• Strong applied ML / data science foundation — reasons from data, designs experiments and evals, and has turned proprietary or structured data into product capability
• E-commerce, marketplace, or two-sided platform context — understanding of both sides of the retailer/brand dynamic
• Experience evolving a read-only assistant into one that takes actions safely — confirm-first patterns, guardrails, and failure containment
• Familiarity with preload-over-RAG context strategies, Snowflake-backed grounding, or hybrid approaches
• Hands-on experience with the OpenAI Agents SDK or similar agentic frameworks in production
• Prior 0→1 / early-stage product experience — has built something meaningful from scratch
• Recommendation, retrieval, or personalization modeling background
• Public writing, open-source contributions, or talks that show structured thinking about agentic / applied-AI systems