Job Title

Were a team of dedicated experts creating a new way to commerce for the age of AI Shopping. fabric is on a mission to build an AI Commerce Operating System to orchestrate, optimize, and scale unified commerce for everyone. Its a system of intelligent actions on top of enterprise-grade products that keep demand with supply in sync and optimize outcomes for margin improvement and delivering brand promise. Headquartered in San Francisco, fabric was founded by a group of industry veterans determined to bring the same technical principles found at Amazon to retail. Leading retailers, including Petmeds, Debenhams, Chicos, Crate & Barrel, and Backcountry, trust fabric to run their modern commerce business. No matter what field you are in, fabric has exciting opportunities for people passionate about making a difference and skilled at what they do! Here are four questions you should ask yourself: Do I believe in fabric''''s mission? Do I prefer iteration over specification, testing and learning over checklists? Am I excited to use AI to experiment and work efficiently? Am I motivated to disrupt e-commerce? If the answer is yes, we want to talk to you! Where we hire: We are a remote-first company, focused on hiring in specific geographic locations. Canada We''''re limited to hiring in the following provinces: Your next career: At fabric, were building the future of commerce with AI agents that help merchants make and save more money. Our mission is to create an agentic commerce operating systemwhere intelligent systems reason, plan, and act across workflows to unlock measurable business outcomes. Were looking for a Senior AI Engineer (SDE III) to play a critical role in architecting, building, and scaling our agentic AI platform and core AI agents. This role goes beyond model developmentyoull help define how AI systems are designed, deployed, evaluated, and evolved across the company. Youll join a small, high-impact team, working closely with our CTO and AI Team, owning core components end-to-end in a true 01 environment. This is an opportunity to shape foundational AI systems at a well-funded startup, with real product ownership and influence on technical direction. Your Responsibilities: Own the full lifecycle of AI/ML solutionsfrom problem framing and system design through training, evaluation, deployment, and iteration in production. Translate ambiguous business problems into scalable AI architectures and agent behaviors. Drive technical decisions around model selection, data strategy, and evaluation frameworks. Design and implement productiongrade agentic systems, including reasoning, planning, orchestration, memory, and tooluse. Ensure systems are reliable, observable, and perform at scale. Improve system reliability through metrics, alerts, drift detection, and performance monitoring. Evaluate and integrate emerging techniques (e.g., LLMs, RAG, knowledgeaugmented systems) with a strong focus on production readiness and ROI. Stay curious and informed. Explore new tools, libraries, and research to keep improving the quality and performance of our AI systems. Help build intelligent data products such as recommendation engines, personalization, categorization, search ranking, forecasting, and fraud detection. Collaborate across teams and work closely with product managers, designers, and software engineers to build AI agents that solve real world problems. What you bring to the table: 7+ years of experience in applied ML / AI engineering, with a strong track record of shipping and operating production AI systems in highgrowth startups. Experience in productfocused environments building end to end SaaS products, where ownership and execution matter. Demonstrated ability to work in ambiguous, 01 problem spaces. Proven experience building and deploying ML models for realworld use cases (e.g., recommendations, search, NLP, categorization). MLOps & Infrastructure: Deployment, monitoring, CI/CD, ML pipelines, containerization, and cloud infrastructure. Fullstack ML expertise across: Modeling (deep learning, classical ML, RL, unsupervised learning). MLOps (deployment, monitoring, CI/CD, infrastructure). Product Building - building endend SAAS products. Experience with Knowledge graphs is a big plus. Bachelors or Masters degree in Computer Science, Engineering, Econometrics/ Statistics, or related fieldor equivalent practical experience. Passion for building usable, scalable products not just research or models. Clear communicator with a collaborative mindset, eager to grow and contribute within a small, fastpaced team. Canadian locations: The base salary for this role is between CAD $160K to 200K annually. #J-18808-Ljbffr