Senior Applied AI/ML Scientist - Faire, Kitchener-Waterloo, ON

AI Summary

This Senior Applied AI/ML Scientist role at Faire in Kitchener-Waterloo, ON, focuses on leading the science and technical direction for Compass, Faire's user-facing AI assistant. Key duties involve driving agent quality through data, evaluation, and modelling, while shipping product features end-to-end. Top requirements include over five years of industry experience building and shipping production ML/AI systems, specifically with agentic or LLM-powered features. This position offers a salary range of $171,000 to $235,500 per year, plus equity and benefits.

About the Role at Faire

Faire is actively building the future of wholesale, connecting independent retailers with brands that will define their stores. At the core of this mission is Compass, Faire’s user-facing AI bet within the Discovery Pillar, which aims to build an always-present, context-aware retailer assistant throughout the engagement journey. This assistant helps retailers make smarter buying decisions by combining Faire’s rich proprietary data with agentic AI and web search, increasingly gaining the ability to take action on retailers’ behalf. As a Senior Applied AI/ML Scientist on the Compass team, you will serve as the science and technical lead for this product, driving agent quality through meticulous data analysis, rigorous evaluation, and advanced modelling, while shipping product features end-to-end with high velocity. This is a deeply hands-on individual contributor role, requiring a keyboard-first approach without direct reports. You will set the data-grounded direction for how the assistant functions, while also acting as a full-stack AI, ML, and backend builder who rapidly transforms ideas into shipped product.

Key Responsibilities and Impact

In this role, you will own the science and technical north star for Compass’s agentic products, encompassing the current retailer assistant and future innovations. This involves strategically leveraging Faire’s proprietary data, defining agent, tool, and context strategies, and establishing methods to measure and enhance agent quality as systems gain the ability to act. You will be responsible for shipping retailer-assistant features end-to-end, utilising AI-native workflows across the FLARE Python app, data plumbing, tool wrappers, and the frontend surfaces where the assistant appears. A crucial aspect of this position is translating ambiguous product bets into sequenced, de-risked tactical plans, prioritising initiatives with the highest impact and probability of success. You will also set and raise the bar for evaluation- and experiment-driven development, defining how the team assesses an agent’s effectiveness, including offline evaluation suites, LLM-as-judge metrics, and specific quality criteria per surface and retailer journey. Making pragmatic engineering choices is essential, ensuring solutions are simple enough to ship immediately yet designed to evolve without being over-engineered for imagined future scale. Collaboration with engineers on architecture and serving trade-offs, and acting as the science/technical interface to adjacent teams like Search, Personalisation, and Platform/FLARE, will be vital. Your contributions will significantly raise the team’s collective judgment through prototypes, analyses, design reviews, and pairing sessions, ensuring continuous product improvement.

Candidate Profile and Qualifications

Faire is seeking a candidate with at least five years of industry experience in building and shipping production ML/AI systems that have demonstrated measurable business impact. This experience must include hands-on ownership of the applied-science side, encompassing data, evaluation, modelling, and quality, rather than solely system plumbing. A strong track record of shipping agentic or LLM-powered features in a core production product is essential, coupled with a deep, opinionated understanding of agent design trade-offs, including evaluation strategy, latency/cost/quality tension, tool-calling versus context preload, guardrails, and failure containment. The ideal candidate possesses a robust applied ML and data science foundation, capable of reasoning from data, designing experiments and evaluations, and transforming proprietary or structured data into powerful product capabilities. Demonstrated ability to ship fast across multiple stacks—backend, data, and ideally frontend—with consistent quality is highly valued, indicating cross-stack range rather than single-layer specialisation. Being AI-native in practice, utilising AI coding tools and agent workflows as a force multiplier, is a key expectation. Architectural maturity, evidenced by the ability to explain design choices that are simple yet scalable, is also important. The role demands high autonomy, resourcefulness, and sound judgment in knowing when to escalate issues versus solving them independently. Fluency in engineering concepts to make informed architectural decisions is also required. Bonus points are awarded for experience in e-commerce, marketplace, or two-sided platform contexts, evolving read-only assistants into action-taking ones safely, hands-on experience with OpenAI Agents SDK or similar frameworks, and familiarity with preload-over-RAG context strategies.