Senior Applied AI/ML Scientist - Marketplace Quality
About this role
Developing pricing-integrity models and large-scale entity resolution systems forms the daily focus for this position on Faire's Marketplace Quality team. Professionals here build algorithmic solutions that match the company catalog against messy external data, extract structured attributes from text and images, and translate model scores into action under a constrained operations budget. Operating in a hybrid structure in San Francisco, the team works to maintain trust for hundreds of thousands of independent brands and retailers by detecting policy violations and ensuring product authenticity.
Faire exists to connect independent retailers and brands globally using data and machine learning to level the playing field against big box competitors. The Marketplace Quality group specifically handles notification systems, recommender algorithms, advertising attribution, and lifetime value predictions. Individuals who excel in this position are resourceful problem solvers capable of driving projects from initial framing through production and impact measurement with limited supervision.
What you will do
- Manage applied machine learning projects end-to-end by framing problems, building and shipping models, and measuring marketplace impact.
- Develop pricing-integrity models that compare catalog items against external pricing signals to detect over- and under-pricing violations.
- Solve product matching and entity resolution at scale using embeddings, retrieval, and multi-modal large language models.
- Extract structured attributes from unstructured listing content and third-party sources to power detection and enrichment systems.
- Design targeting and prioritization logic that ranks violations by expected marketplace impact against false positive costs.
- Build human-in-the-loop systems with operations partners to design audits, generate training labels, and set precision bars.
- Design and analyze experiments for enforcement levers such as downranking and badging to measure effects on retailer trust.
What you need
- Bring 3 or more years of industry experience using machine learning to solve real-world problems.
- Demonstrate past involvement with relevant business problems in e-commerce, marketplaces, catalog quality, search, or personalization.
- Apply technical methods including deep learning, large language models, computer vision, entity resolution, and causal inference.
- Possess strong programming skills and an eagerness to learn new tools and techniques.
- Show the capability to drive projects independently and lead model development with limited supervision.
- Communicate effectively and collaborate within a highly cross-functional team environment.
Nice to have
- 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
Benefits
- Competitive pay
- Equity
- Comprehensive benefits designed to support life inside and outside of work
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing. Additionally, hybrid in-office roles have the flexibility to work remotely up to 4 weeks per year.
What is the salary range for the position?
The pay range for this role is $211,000 to $290,500 per year in San Francisco. Actual base pay is determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location.
What tools and technologies will I be working with?
You will work across structured and unstructured data using entity resolution, information extraction, multi-modal LLMs, calibrated classification, constrained optimization, and experimentation. The company also invests in the latest enterprise AI tools to help team members work smarter.