<div>About Faire 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, but individually, they are small compared to these massive entities. At Faire, were 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 that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. 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. </div>About this role What Youll Do: <ul> <li>Own the next-gen recommendation engine, combining multi-stage ranking, LLMs, deep personalization embeddings, reinforcement learning, and generative recommendation to serve personalized product recommendations (e.g. feed) in &lt;100ms latency.</li> <li>Design and productionize agentic systems for discovery, enabling AI agents to generate personalized product explorations, provide explainable recommendations, and assist retailers in browsing, filtering, and evaluating products and brands.</li> <li>Drive technical strategies to advance various key business priorities, such as cold-start recommendations and long-term value (LTV) optimization, working across Discovery to help retailers find the right brands and cultivate lasting relationships on Faire.</li> <li>Lead end-to-end model development and deployment efforts and experimentation, working with best-in-class frameworks to scale inference reliably.</li> <li>Mentor and grow other senior Applied Scientists and MLEs, and establish best practices around deep learning model development, LLM / agent workflow evaluation, and MLOps.</li> </ul> Qualifications <ul> <li>7+ years of experience building large-scale AI/ML systems, with 3+ years in recommender systems, search, or ads ranking.</li> <li>Hands-on expertise with deep learning libraries (e.g. PyTorch), retrieval infrastructures (e.g. ElasticSearch, Faiss, ScaNN, Pinecone), and real-time data processing feature infrastructures (e.g. Kafka, Flink).</li> <li>A proven track record of shipping STOA ML models that blend large language models (e.g., BERT, GPT-class) to power personalization.</li> <li>A product mindset with a bias for action—you’re comfortable going from paper to prototype to production in days with a deep understanding of user problems.</li> <li>Strong coding skills in Python, good engineering sense to code in production environments, a deep respect for reliability and ownership, and experience working in high-stakes production environments.</li> <li>Clear communication and a history of cross-functional impact—you influence beyond your team and help set the technical bar.</li> </ul> Great to Haves: <ul> <li>Contributions to open-source ML libraries or peer-reviewed publications in ML/AI.</li> <li>MS or PhD in AI, Computer Science, Statistics, or a related STEM field.</li> </ul> Salary Range: California &amp; New York: the pay range for this role is $224,000 to $308,000 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 provided is subject to change and may be modified in the future. <div>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 <ul> <li>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.</li> <li>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.</li> <li>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.</li> <li>We are curious and resourceful: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality.</li> </ul> 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<a href="https://blog.faire.com/"> blog</a>. 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 <a href="https://docs.google.com/forms/d/e/1FAIpQLSczpI9me7nYKhunSC6AlagHKv75LHqfi-Qi2TP_3dpin0Uc9Q/viewform">Accommodation Request Form</a> (https://bit.ly/faire-form)</div>

Faire

Staff Applied AI/ML Scientist - Personalization & Recommendation

Faire

Similar Remote Jobs