Staff Product Manager, AI, Data, ML & Platform

About Faire

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. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.

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 this role

We're looking for a Staff Product Manager to treat Faire's data and ML platforms as products: define clear user needs, measurable outcomes, and a roadmap that accelerates every team building on these foundations. You will make data trustworthy by default and make ML capabilities easy to adopt and operate in production. When the data Faire runs on is high-quality and easy to use, every team that depends on it makes better decisions and ships better products.

ML powers Faire's marketplace, from search and recommendations to personalization. Shared data sits underneath nearly every product decision we make, and AI data agents are how that insight shows up in the day-to-day of building product. This role sits at that foundation: you will run structured discovery with the engineers, scientists, and product teams who build on these platforms, prioritize the investments that unblock them, and hold the bar on quality, reliability, and adoption. You will also establish the product-platform operating model — roadmap, intake, adoption, measurement — that Faire can extend as the broader product platform function scales.

What you’ll do

• Own the path from ML idea to production: run structured discovery with ML engineers and data scientists, prioritize platform investments across training, deployment, observability, and model registry, and drive adoption so usage signals shape engineering priorities.
• Measure ML platform success through user outcomes, including time saved, models shipped, and support load, rather than process metrics alone.
• Scale the use of AI data agents: own the vision for agentic data insights Product, Design, and Engineering use to shape strategy; prioritize the data-engineering investments agents depend on; drive adoption across product development; and measure success through decisions accelerated and time saved on insight work, not demo metrics.
• Increase trust in Faire's core data by defining SLAs, driving pipeline health on datasets the business depends on, establishing data contracts and clear dataset ownership, and making the cross-functional case for quality investments that compete with feature work.
• Improve metadata quality, lineage, and discovery so teams can find, trust, and trace data, and establish access controls and durable compliance processes for data deletion, access management, and auditability.
• Build the data products internal users actually need through embedded discovery: fact and dimension tables, semantic layers, reusable data products, and first-class discoverability and developer experience.
• Establish a durable product-platform operating model with a published roadmap the company can plan against, a clear intake process, adoption tracking across usage, retention, and satisfaction, and platform cost and efficiency as a health metric — proven first on data and ML, and built to extend as Faire's product platform function grows.

Qualifications

• 8+ years of product management experience, with ownership of complex technical products at Staff or Lead scope.
• Technical fluency across data and ML platforms: you have worked directly with data and ML systems, can engage credibly with engineering on architecture tradeoffs, understand modern ML in production (model training and deployment, feature pipelines, evaluation), and translate technical complexity into clear strategic choices.
• Experience shipping platform products for internal technical users, including discovery, success metrics, adoption, and operational discipline.
• Experience shipping in a marketplace or other data-rich product context where the product depends on data feeding algorithmic choices such as search, recommendations, or personalization. You understand how upstream data and ML decisions translate into conversion, relevance, and retention.
• Cross-functional influence and strategic prioritization: credibility with senior engineers and data scientists, the ability to negotiate without positional authority, ruthless sequencing, and clarity about what the team is choosing not to do.
• Comfort building an operating model, not just a roadmap: you have established discovery, intake, and measurement practices that other teams could adopt, and you are energized by growing a platform practice beyond its initial domain.

Salary Range

San Francisco, CA and New York City, NY: The pay range for this role is $220,000 - $302,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors, including transferable skills, work experience, market demand, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.

Why you’ll love working at Faire

• 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.

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 (

Privacy

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 (