Senior Applied ML/AI Scientist - Search

Faire

Kitchener-waterloo, On; Toronto, On
Onsite
Posted 2026-07-02

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Skills named in this job

LLMs
Machine learning
Python

Read from the description itself, not inferred.

This role on the market

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Faire has 74 open roles listed here.

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Preparing for this interview

Interviews for scientist roles keep coming back to Python, Machine learning, SQL, LLMs. Practise those questions before you sit with Faire.

Questions you are likely to be asked

Why do you want to join Faire?
What is your experience with LLMs? Tell me one thing you learned the hard way.
When would you not use machine learning for a problem?
Walk me through a model you built, from the data to how it was used.
How did you know your model was actually good, and not just good on your test set?

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About Faire

Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. 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 businesses can grow and local communities can thrive.

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.

About the role

Search is how retailers do their jobs on Faire. Wholesale queries and retailer expectations look different from consumer e-commerce and the right product depends on the store's category, price point, and aesthetic. When we get it wrong, it costs real money.

The Search algorithms team owns everything between the click on the search bar and the final ranker: typeahead and empty-state suggestions, query understanding, retrieval across five-plus independent sources, relevance modeling, and result-page surfaces like carousels and refinements. Within our scope, scientists own components outright: when you own query understanding here, you own the models, the roadmap, and the metrics.

You'll work across the full modern search stack: transformer-based embedding retrieval serving live traffic, LLMs powering query understanding and query rewriting, fine-tuned vision-language models scoring relevance, and graph-based retrieval — with generative retrieval and semantic IDs on the horizon.

What you'll do

Contribute to the next-generation Search engine, integrating LLMs, query understanding, dense vector retrieval, deep personalization embeddings, multi-stage ranking, and reinforcement learning to serve personalized product feeds with <100ms latency.
Own one or more search components end to end: problem framing, modeling, production code, experiment design, and the call on what to build next.
Ship to live traffic and let A/B tests, not opinions, settle what works.
Raise the team's bar through design reviews, pairing, and honest post-mortems

You're a great fit if you have

3+ years building production ML systems, with meaningful time in search, recommendations, or another retrieval-and-relevance domain.
Shipped models that served real traffic, and owned the experiments that proved (or disproved) their value.
Depth somewhere in the modern retrieval stack — dual encoders and ANN serving, LLM-based query understanding, learning-to-rank fundamentals — and the ability to pick up the rest.
Strong Python and the engineering chops to take your own models to production.
Clear communication with scientists, engineers, and PMs: you make a crisp case for your ideas and update quickly when someone has a better one.
Excellent product judgment to connect customer and business context to technical decisions

Bonus Points

Marketplace or e-commerce experience
Publications, open-source work, or public writing on search and recommender systems.
MS or PhD in CS, Statistics, or a related field.

Salary Range

Canada: the pay range for this role is $180,000 to $247,500 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.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and 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.

Why you’ll love working at Faire

Flexible paid time off, plus company-wide recharge days
Paid parental leave up to 16 weeks
Comprehensive health coverage, with options varying by location
Fertility and family-building benefits through Carrot, including a lifetime reimbursement allowance
Mental health support and coaching through Modern Health
Stipends to support your professional development, wellness, and work from home needs
Employer-sponsored retirement plan, dependent on country of employment

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)

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Listed on greenhouse · posted 2026-07-02. ApplySarthi collects openings and links to application pages; the role is advertised by Faire, not by us.