Senior Data Scientist​/Machine Learning Engineer - Listing Quality

Senior Data Scientist / Machine Learning Engineer - Listing Quality

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Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined. At Faire, we use technology, data, and machine learning to connect entrepreneurs and level the playing field for small businesses.

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry. As a member of the Brand Data Science team working on Listing Quality, you will be responsible for improving the quality of product listings to help retailers find and evaluate products on Faire. You will use ML and AI to tackle critical challenges such as enhancing image and text quality, extracting structured product attributes, and accurately identifying duplicates and product variants.

You will leverage deep learning, multi‑modal LLMs, and human‑in‑the‑loop training to create high‑performance solutions.

• Drive data science vision, strategy, and execution on Listing Quality, using ML and AI solutions to improve the quality of Faire’s product listings.
• Use deep learning, LLM fine tuning, and human‑in‑the‑loop training to automatically detect and address issues with high accuracy.
• Act as a lead on the cross‑functional Listing Quality pod, thinking end‑to‑end about brand and retailer experiences.

• 3+ years of industry experience using machine learning to solve real‑world problems.
• Experience with relevant business problems (e.g., e‑commerce).
• Experience with relevant technical methods (e.g., LLM fine tuning, deep learning, or human‑in‑the‑loop machine learning).
• Strong programming skills.
• An excitement and willingness to learn new tools and techniques.
• The ability to design and implement ML solutions without supervision.
• Strong communication skills and the ability to work in a highly cross‑functional team.

• Master’s or PhD in Computer Science, Statistics, or related STEM fields is highly recommended.
• Previous experience in listing quality for e‑commerce.
• Previous experience in supervised fine tuning of multi‑modal LLMs.
• Experience deploying and optimizing LLM inference systems at scale (10B+ tokens), with focus on cost efficiency and product impact.

Canada: the pay range for this role is $168,000 to $231,000 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 is subject to change.

Hybrid Faire employees currently go into the office 2 days per week on Tuesdays and Thursdays. Effective starting in January 2026, employees will be expected to go into the office on 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 indicated in the job posting.

Applications for this position will be accepted for a minimum of 30 days from the posting date.

• We are entrepreneurs:
  Faire is being built for entrepreneurs, by entrepreneurs. We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is taking part in the founding process.
• We are using technology and data to level the playing field:
  We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 small business owners.
• We build products our customers love:
  Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie — not steal a piece from it. Running a small business is hard work, but using Faire makes it easy.
• We are curious and resourceful:
  Inquisitive by default, we explore…