Position: Staff Data Scientist / Machine Learning Engineer - Listing Quality
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. At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive.
As the Listing Quality lead within the Brand Data Science team, 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.
This space has been evolving rapidly with advancements in AI and you will be at the forefront of applying the latest technology to drive real-world impact. You will mentor and guide the work of more junior Data Scientists, while also executing as an individual contributor. You will define the ML roadmap and be the Data Science lead within the cross-functional Listing Quality pod, working with product, design, engineering, analytics, and operations to solve problems end-to-end.
What you’ll do
• 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.
• Mentor and guide the work of more junior Data Scientists.
• Act as a lead on the cross-functional Listing Quality pod, thinking end-to-end about brand and retailer experiences.
Qualifications
• 5+ 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
• Demonstrated ability to mentor Senior Data Scientists, develop team strategy, and independently lead model development
• Strong communication skills and the ability to work in a highly cross-functional team
Great to Haves:
• 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
Salary Range
Canada: the pay range for this role is $196,000 to $269,500 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 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 will be indicated in the job posting.
Applications for this position will be accepted for a minimum of 30 days from the posting date.
Why you’ll love working at Faire
• 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…
