Senior Applied AI/Machine Learning Scientist (Compass)

Join Faire as a Senior Applied AI/Machine Learning Scientist on the Compass team. In this hands-on role, you will drive agent quality through data, evaluation, and modeling, while shipping product features end-to-end. You will shape the future of the retailer assistant, leveraging Faire's proprietary data and defining how to measure and raise agent quality. You will also partner closely with engineers and act as the science/technical interface to adjacent teams.

• Lead the development and implementation of AI and machine learning models to enhance agent quality and drive product features.
• Collaborate with cross-functional teams to translate product requirements into actionable plans, ensuring timely delivery of features.
• Establish and maintain evaluation and experimentation frameworks to measure the effectiveness of AI-driven solutions.
• 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
• Architectural maturity — can explain design choices that work simply today but won’t need to be thrown away when requirements grow
• AI-native in practice: uses AI coding tools and agent workflows as a force multiplier in day-to-day work
• Fluent enough in engineering to make sound architecture calls
• Operates with high autonomy and resourcefulness, with good judgment about when to escalate and when to just solve it
• 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
• 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
• Hands-on experience with the OpenAI Agents SDK or similar agentic frameworks in production
• Familiarity with preload-over-RAG context strategies, Snowflake-backed grounding, or hybrid approaches
• 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