Senior Applied AI/ML Scientist - Compass

About Faire

Faire is a technology wholesale platform connecting independent retailers with brands through technology, data, and machine learning.

About the Role

As a Senior Applied AI/ML Scientist on Compass, Faire’s user-facing AI initiative within the Discovery Pillar, you will lead the science and technical direction of a context-aware retailer assistant. Compass combines proprietary data, agentic AI, and web search to support buying decisions and increasingly take action on retailers’ behalf.

This is a deeply hands-on individual contributor role with no direct reports. You will drive agent quality through data, evaluation, and modeling while shipping features across AI, ML, backend, and frontend surfaces.

Responsibilities

• Own the science and technical direction for Compass’s agentic products, including proprietary-data usage, agent/tool/context strategies, and quality measurement as agents gain action-taking capabilities.
• Ship assistant features end-to-end across the FLARE Python application, data plumbing, tool wrappers, and frontend surfaces using AI-native workflows.
• Translate ambiguous product opportunities into sequenced, de-risked plans, prioritizing impact and probability of success.
• Establish evaluation- and experiment-driven development, including offline evaluation suites, LLM-as-judge metrics, and quality criteria for different surfaces and retailer journeys.
• Make pragmatic engineering decisions that support fast delivery and future evolution without unnecessary complexity.
• Partner with engineers on architecture and serving tradeoffs and collaborate with Search, Personalization, and Platform/FLARE teams.
• Improve team judgment through prototypes, analyses, design reviews, and pairing.

Qualifications

• At least 5 years of industry experience shipping production ML/AI systems with measurable business impact, including hands-on ownership of data, evaluation, modeling, and quality.
• Experience shipping agentic or LLM-powered features in a core production product, with deep understanding of evaluation strategies, latency/cost/quality tradeoffs, tool-calling versus context preload, guardrails, and failure containment.
• Strong applied ML and data science foundation, including data-grounded reasoning, experiment and evaluation design, and turning proprietary or structured data into product capabilities.
• A track record of delivering quickly and with quality across backend and data stacks; frontend experience is ideal.
• Practical use of AI coding tools and agent workflows in daily work.
• Architectural maturity and engineering fluency to make sound design decisions that meet current needs and can evolve.
• High autonomy, resourcefulness, and good judgment about when to escalate versus solve independently.

Preferred Qualifications

• E-commerce, marketplace, or two-sided platform experience.
• Experience safely evolving read-only assistants into action-taking systems using confirmation patterns, guardrails, and failure containment.
• Production experience with the OpenAI Agents SDK or similar agentic frameworks.
• Familiarity with preload-over-RAG context strategies, Snowflake-backed grounding, or hybrid approaches.
• Experience building meaningful products from scratch in early-stage environments.
• Recommendation, retrieval, or personalization modeling experience.
• Public writing, open-source contributions, or talks demonstrating structured thinking about agentic or applied-AI systems.

Location and Work Arrangement

• Locations: New York City, NY, or San Francisco, CA.
• Hybrid employees attend the office three days per week: Tuesday, Thursday, and a flex day chosen from Monday, Wednesday, or Friday.
• Hybrid in-office roles may work remotely for up to four weeks per year.

Compensation and Benefits

• US annual base salary: $211,000–$290,500.
• Eligible for equity and comprehensive benefits.
• Actual base pay depends on transferable skills, experience, market demands, and primary work location. The range is subject to change.

Application Information

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