Senior Applied AI/ML Scientist - Compass

About the Position

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. 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.

About the Role

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 Compass - Faire’s user-facing AI bet within the Discovery Pillar - building an always-present, context-aware retailer assistant along the engagement 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.

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. 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.

Responsibilities

• Own the science and technical north star 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.
• Ship retailer-assistant features end-to-end - 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.
• Translate ambiguous product bets into sequenced, de-risked tactical plans - what to build now, what to defer, and which bets carry the highest impact × probability-of-success.
• Set and raise the bar for eval- and experiment-driven development - 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.
• Make pragmatic engineering choices: simple enough to ship now, designed to evolve - not over-engineered for imagined future scale, but not throwaway either.
• Partner closely with engineers on architecture and serving tradeoffs, and act as the science/technical interface to adjacent teams (Search, Personalization, Platform/FLARE).
• 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 - including hands-on ownership of the applied-science side (data, evaluation, modeling, quality), not just system plumbing.
• 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.
• AI-native in practice: uses AI coding tools and agent workflows as a force multiplier in day-to-day work.
• Architectural maturity - can explain design choices that work simply today but won’t need to be thrown away when requirements grow.
• Operates with high autonomy and resourcefulness, with good judgment about when to escalate and when to just solve it.
• Fluent enough in engineering to make sound architecture calls.

Nice to Have

• 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.

We Offer

• Competitive salary: Canada: the pay range for this role is $180,000 to $247,500 per year.
• Equity and benefits: This role will also be eligible for equity and benefits.
• Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
• Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.
• Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.
• Real rewards: Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.
• Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.

About the Company

Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog.

Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression.

Faire is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form)

For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)