<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>Faire is using machine learning and AI to change wholesale and help local retailers compete with Amazon and big box stores. Our experienced team of engineers and data scientists are developing solutions related to discovery, ranking, search, and recommendations - all with the goal of helping local retail thrive. Our Engineering team in collaboration with the Data team owns a wide variety of machine learning systems that power the marketplace. We care about good engineering practice and love to write software that is secure, tested, easy to maintain, and can scale to millions of users. We build minimum viable products, deploy to production multiple times per day, A/B test customer-facing changes, listen to the data, and iterate. As a technical leader on the Discovery Infra and Enablement team, you will be responsible for building and scaling the next generation of Search and AI infrastructure at Faire. You’ll unlock new agentic features, enable cutting-edge retrieval and ranking applications, and ensure our search experience is world-class in both relevance and latency. You’re excited about this role because… <ul> <li>Build and optimize the next-generation Discovery Search and AI stack, integrating LLMs, semantic search, dense vector retrieval, embeddings, and reinforcement learning to serve personalized results with &lt;100ms latency.</li> <li>Lead the design and productionization of natural language search and intelligent discovery systems, enabling AI agents to generate relevant collections, explain results, and support retailers in browsing, filtering, and evaluation.</li> <li>Tackle deep technical challenges in query understanding, retrieval efficiency, and multi-stage ranking pipelines.</li> <li>Develop infrastructure to support state-of-the-art search algorithms while optimizing for cost, speed, and accuracy at scale.</li> <li>Shape the technical strategy for how applied AI is leveraged across search and discovery at Faire.</li> <li>Collaborate with Data Scientists, Product Managers, and ML Engineers to translate business needs into scalable technical solutions.</li> </ul> Qualifications <ul> <li>8+ years of industry experience building and scaling distributed systems, with strong fundamentals in distributed infra.</li> <li>5+ years working on search, ranking, or query understanding systems at scale.</li> <li>Hands-on experience with traditional, semantic, and hybrid search frameworks (e.g. Elasticsearch, Lucene, Solr, FAISS, Vespa, Weaviate, Milvus).</li> <li>Expertise in search indexing, retrieval, and ranking pipelines, including query rewriting, personalization, embeddings, and vector search.</li> <li>Experience with ML-driven search systems, including personalization, re-ranking, and reinforcement learning.</li> <li>Strong knowledge of latency optimization and building low-latency, high-throughput serving systems.</li> <li>Familiarity with AI/ML infra such as model serving (e.g. TensorFlow Serving, TorchServe, Ray Serve), feature stores, and online experimentation.</li> <li>Proficiency in at least one modern backend language (Java, Kotlin, Go, or Python).</li> <li>Solid understanding of APIs, distributed data storage, and streaming systems (e.g. Kafka, Flink, Spark).</li> <li>Excellent communication skills and a proven track record of technical leadership and mentorship.</li> </ul> Great to have <ul> <li>Experience with applied LLM integration into production systems (agentic, multi-turn chat experience,  conversational search, etc).</li> <li>Contributions to open-source search/ML infra projects.</li> </ul> <h3>Salary range:</h3> Canada: The pay range for this role is $216,000 - 297,000 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

Senior Staff Backend Engineer - Discovery AI Infrastructure & Enablement

Faire

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