Description
Faire is building the future of wholesale, connecting independent retailers with brands that 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 engagement journey.
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.
Responsibilities:
- Own the science and technical north star for Compass's agentic products , the retailer assistant today and whatever comes next
- Ship retailer-assistant features end-to-end , across the FLARE Python app, data plumbing, tool wrappers, and the frontend surfaces where the assistant appears
- Translate ambiguous product bets into sequenced, de-risked tactical plans
- Set and raise the bar for eval- and experiment-driven development
- Make pragmatic engineering choices
- Partner closely with engineers on architecture and serving tradeoffs
- Raise the team's collective judgment through prototypes, analyses, design reviews, and pairing
Requirements:
- 5+ years of industry experience building and shipping production ML/AI systems with measurable business impact
- Has shipped agentic / LLM-powered features in a core production product
- Strong applied ML / data science foundation
- Track record of shipping fast across multiple stacks
- AI-native in practice
- Architectural maturity
- Operates with high autonomy and resourcefulness
Nice to have:
- E-commerce, marketplace, or two-sided platform context
- Experience evolving a read-only assistant into one that takes actions safely
- 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
- Recommendation, retrieval, or personalization modeling background
- Public writing, open-source contributions, or talks that show structured thinking about agentic / applied-AI systems