Senior Applied AI/ML Scientist - Ads

Faire; Kitchener; Hybrid; CAD 168,000 - 231,000; Full time

Job summary

A leading online wholesale marketplace is seeking a Machine Learning Engineer to innovate their advertising products. In this role, you will develop models, optimize auction systems, and contribute to a collaborative team driving real business outcomes. Ideal candidates have 4+ years of experience in machine learning, strong programming skills, and a passion for solving real-world problems. This position is hybrid, allowing flexibility between office and remote work.

Benefits

Equity and benefits

Flexible remote work options

Collaborative work environment

Qualifications

• 4+ years of industry experience using machine learning to solve real-world problems.
• Demonstrated ownership of the full ML lifecycle.
• Strong programming skills in relevant languages.

Responsibilities

• Own the end-to-end development of ML models.
• Build best-in-class models in ads retrieval, ranking, auction optimization.
• Help define the roadmap and technical culture of the Ads Data team.

Skills

Machine learning

Programming skills

SQL

Communication

Education

Master’s or PhD in Computer Science, Statistics, or related STEM fields

Job description

Overview

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.

About this role

The Ads Data team is building the next generation of advertising products for the wholesale industry. As a key member of this team, you’ll shape the future of our ads system at Faire—from retrieval and ranking to auction design and bidding strategy. Our in-house stack gives us unique end-to-end ownership, and our first-party conversion data allows us to train highly effective models and optimize for real, business-critical outcomes on behalf of our brands.

This is a rare opportunity to be an early contributor to a fast-growing team in an incredibly strategic area of the business. Ads is a major company priority and you\'ll have a massive impact in shaping the platform that connects independent retailers and brands. You’ll work closely with engineers, product managers, and designers to launch and iterate on systems that are not just technically sophisticated, but also directly drive improvements to the bottom line for both brands and Faire.

What you’ll do

• Own the end-to-end development of ML models across the ads stack—from problem framing, solution design, modeling, and implementation to deployment, experimentation, and impact measurement
• Build best-in-class models in areas such as ads retrieval, ranking, auction optimization, bidding, and pacing—drawing from techniques in search/recommendation systems and marketplace optimization
• As an early member of the Ads Data team, help define its roadmap and technical culture, leveraging deep product intuition to shape what ads at Faire should be—not just how they’re built
• Work in a fast-paced, collaborative environment with team members who’ve shipped ML at top tech companies (e.g. Uber, Airbnb, Meta, Amazon, Pinterest)

Qualifications

• 4+ years of industry experience using machine learning to solve real-world problems, ideally in Ads, Search, Recommendations, or a related large-scale production domain
• Demonstrated ownership of the full ML lifecycle: from scoping and design through training, deployment, A/B testing, and iteration
• Strong programming skills
• An excitement and willingness to learn new tools and techniques
• Experience with relational databases and SQL
• The ability to contribute to team strategy and to lead model development without supervision
• Strong communication skills and the ability to work with others in a closely collaborative team environment

Great to Haves

• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
• Ability to quickly implement state of the art algorithms from an academic paper

Salary Range

Canada: the pay range for this role is $168,000 to $231,000 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.

Workplace and Scheduling

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.