Co-op AI Platform Engineer - Winter 2026 at Faire

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

26 Jan, 26

Salary

52.0

Posted On

28 Oct, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Kotlin, Git, SQL, Airflow, Spark, Snowflake, Redis, DynamoDB, Terraform, AWS

Industry

Software Development

Description

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, we're 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. Work Term: Winter 2026 (January 5 - April 24) Role Description The AI Platform team’s mission is to make machine learning and AI at Faire fast, accurate, and accessible to all. Its goal is to enable teams at Faire to seamlessly develop and deploy a wide variety of machine learning and AI models that power Faire’s two-sided marketplace. The team's work spans systems such as feature store, model training and fine-tuning platform, model store, model inference service, model evaluation and model monitoring. More recently, we are also exploring the space of AI tools, agents in order to drive customer impact. Why you’ll love working at Faire As a Faire co-op, you’ll have the opportunity to: Take on the responsibilities of a full-time engineer and be treated like a permanent Faire employee from day one Work alongside talented engineers and engineering leaders, product managers, and designers on a daily basis to contribute to real projects with real impacts Be challenged to learn and grow in a fast-paced environment with supportive team members Own projects from scoping to shipping and see how your work directly impacts our customers and drives data-focused results Learn more by checking out the internships section of Faire’s The Craft blog or watching this video by a former Faire co-op. Job Responsibilities: As a member of our ML Platform team, you’ll play a crucial role in developing and enhancing Faire’s machine learning infrastructure, directly impacting our user experience and business success. Your role will involve: Building Essential AI and ML Tools: Create and refine tools and infrastructure to streamline the workflows of our data scientists and machine learning engineers Developing Scalable AI and ML Pipelines: Utilize modern tools like Airflow, AWS Batch, Spark, GPU infrastructure to build scalable machine learning pipelines for essential AI/ ML workflow components such as feature engineering, model inference etc Project Design and Ownership: Based on your interests and expertise, lead a significant project that advances a specific aspect of our AI infrastructure, contributing to our evolving platform. Core Technologies: Python, Kotlin, Git, SQL, Airflow, Spark, Snowflake, Redis, DynamoDB, Terraform, AWS. Learning Environment: We're committed to teaching and growing your skills in these technologies and beyond. Examples of projects our team works on: Enabling batch model inference at scale Enhancing the feature store architecture and saving storage costs by optimizing the underlying data storage layer Streamlining model deployment processes to accelerate ML iteration and speed experimentation Other Requirements: In your last Co-op work term, you received an excellent or outstanding You are earning an 80%+ in your computer science courses or have shown significant improvements in grades over the course of your academic career. We're at an exciting stage in our journey to build a state-of-the-art AI Platform. As a co-op student with us, you’ll gain hands-on AI and MLOps experience and the unique opportunity to influence and shape the future of machine learning at Faire. About our selection process If your application is selected, our Talent Team will reach out to you via email to invite you to: Step 1: Technical Interview (1 hr) Meet 1:1 with an experienced ML engineer to Discuss your ML work experience and background. Be prepared to do a technical deep dive into a past machine learning project of yours. Bonus if it leans towards ML Ops or ML infrastructure but not a requirement! Work through a pair programming exercise together. Use this CodeSignal link to prep for the technical interview (this is entirely optional). If you’re selected to advance beyond the technical interview, our Talent Team will follow up with you via email to invite you to: Step 3: Phone Call with Recruiter (15 mins) Chat 1:1 with a Faire recruiter to get your questions answered. Our recruiter will also share some additional details, including what to expect in terms of compensation. About our work model This role will be in-office on a hybrid schedule. Starting January 13, 2026, all office employees will be expected to come into the office every Tuesday, Thursday, and an Employee-chosen 3rd in-office day (Monday, Wednesday, or Friday). Co-op interns must be aligned with the Kitchener-Waterloo or Toronto offices and live within a 56 km radius of one of the offices. Our offices are centrally located at 85 Willis Way in Uptown Waterloo and 420 Wellington Street West in Toronto’s Fashion District. Competitive Pay & Perks That Go Beyond At Faire, our co-op salaries range from $32-$52 CAD per hour. But that's just the beginning! Here's what else you can look forward to: 3 Paid Time Off (PTO) Days - Because we know rest matters. Commuter Stipend - Helping you get to and from the office stress-free. Phone & Internet Stipend - Stay connected, on us. 80 Hours of Sick Time - Your well-being comes first. Catered Meals & Snacks - Enjoy delicious food and an impressive selection of drinks in the office. Community & Fun - Connect with fellow co-ops and Faire employees through Slack channels, game nights, and exciting social events. Targeted Degrees and Disciplines: Computing: Software ENG - Electrical and Computer Engineering ENG - Software Engineering MATH - Computer Science MATH - Data Science Application Documents Required: Résumé; we don't require a cover letter, but please make it easy for us to see any experience you have with Machine Learning in your resume. 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 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. 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. 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. 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 blog. 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 Accommodation Request Form (https://bit.ly/faire-form) Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

Responsibilities

As a member of the ML Platform team, you will develop and enhance Faire’s machine learning infrastructure, impacting user experience and business success. Your role includes building essential AI and ML tools and developing scalable AI and ML pipelines.