Senior Applied AI/ML Scientist - Listing Quality San Francisco, CA

Faire Wholesale Inc.; San Francisco (CA); Hybrid; USD 211,000 - 291,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work model with remote time

Job summary

Faire is seeking a Senior Applied AI/ML Scientist to own the modeling and measurement for listing quality across millions of products. You will work with unstructured data using multi-modal deep learning and LLMs, pushing projects from framing through production and measurement.

You will partner with product, engineering, design, and analytics to turn models into real business impact. You will influence imagery, titles, descriptions, and structured attributes, driving improvements across search,

Qualifications

• 3+ years of ML industry experience.
• Experience with ecommerce, marketplaces, catalog/content quality, search, or personalization.
• Experience with deep learning and LLMs, CV, information extraction, ranking, or causal inference.
• Willingness to learn new tools and techniques.
• Ability to drive end-to-end projects with limited supervision.
• Strong cross-functional communication.

Responsibilities

• Own ML projects end-to-end: framing, building, shipping, and measuring impact.
• Use multi-modal DL and LLMs to analyze listing content and extract attributes.
• Improve imagery via hero image selection, ordering, cropping, and enhancement.
• Build ranking/exploration methods to optimize content performance.
• Improve listing text and product information coverage and measure impact on discovery.
• Develop LLM-as-judge and human-in-the-loop evaluation systems.
• Collaborate across product, engineering, design, and analytics to ship product.
• Solve challenges in a two-sided marketplace.

Skills

Machine learning

Cross-functional collaboration

Communication

LLMs & multi-modal

End-to-end leadership

Model evaluation

Education

Master's or PhD in CS/Statistics

PhD preferred in CS/Statistics

Job description

Senior Applied AI/ML Scientist - Listing Quality

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

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of Applied AI/ML Scientists specialize in developing algorithmic solutions for notification and recommender systems, advertising attribution, and LTV predictions. We are dedicated to building machine learning models that help our customers thrive.

As a Senior Applied AI/ML Scientist on the Listing Quality team, you will own the modeling and measurement for the content that powers Faire's catalog: the images, titles, descriptions, and structured attributes across millions of products from hundreds of thousands of independent brands. Listing quality is one of the highest-leverage surfaces on the marketplace. Better images and richer product information make products easier to find, easier to evaluate, and easier to buy, and they compound across search, recommendations, and the product detail page. You will work primarily with unstructured data using multi-modal deep learning and LLMs, and you will drive projects end-to-end from framing through production and measurement.

Our team already includes experienced Applied AI/ML Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists and machine learning engineers, and you will help take us there!

What you’ll do

• Own applied ML projects end-to-end: framing the problem, building and shipping the model, and measuring impact.
• Use multi-modal deep learning and LLMs to understand listing content, extract structured product attributes, and detect quality issues at catalog scale.
• Improve product imagery through hero image selection, image ordering, cropping, and enhancement, so that the best representation of a product is the one retailers see.
• Build ranking and exploration approaches (e.g. bandit-style selection) that learn which content performs best for which audience.
• Improve listing text: titles, descriptions, and product information coverage, and measure the downstream effect on discovery and conversion.
• Build LLM-as-judge and human-in-the-loop evaluation systems, and hold them to a measurable accuracy bar before they gate production decisions.
• Partner across product, engineering, design, and analytics to turn models into shipped product and business impact, and to drive brand-facing nudges that improve listings at the source.
• Solve challenging problems related to a two-sided marketplace.

Qualifications

• 3+ years of industry experience using machine learning to solve real-world problems.
• Experience with relevant business problems (e-commerce, marketplaces, catalog and content quality, search, or personalization).
• Experience with relevant technical methods (deep learning and LLMs, computer vision, information extraction, entity resolution, ranking, and/or experimentation and causal inference).
• An excitement and willingness to learn new tools and techniques.
• The ability to drive a project end-to-end and lead model development with limited supervision.
• Strong communication skills and the ability to work in a highly cross-functional team.

Great to Haves:

• Highly recommended: Master's or PhD in Computer Science, Statistics, or related STEM fields.
• Previous experience with catalog quality, product attribute extraction, computer vision for e-commerce imagery, or search and discovery for a two-sided platform.
• Experience building and validating LLM evaluation pipelines, including prompt iteration against labeled data and human-in-the-loop workflows.

Salary Range

San Francisco: the pay range for this role is $211,000 to $290,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, 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 ( https://bit.ly/faire-form)

U.S. Demographic Questions

We believe the future is local. At the core of that future are our customers, a diverse and unique community of entrepreneurs who have taken a chance on themselves. We’re inspired by these dreamers who work today and every day for a better future for their communities. We are committed to building a community and sense of belonging here at Faire that reflects the customers we serve. We want to ensure that we hold ourselves to that commitment externally and internally because differences in perspectives, experiences, and ideas are what make us successful.

Below is a set of voluntary demographic questions that are a part of our Diversity and Inclusion initiatives. If you choose to fill them out, the responses will be used (in aggregate only ) to help us identify areas for improvement in our process. Your responses, or your choice to not respond, will not be associated with your specific application and will not , in any way, be used in the hiring decision.