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 the role

At Faire we build elegant and efficient products to deliver superior customer experiences and enhance marketplace efficiency at the same time. From the mobile checkout process, to personalized search ranking, to the intelligent underwriting engine that determines credit limits for retailers --- we use data and machine learning to constantly iterate and innovate our product offering to create more value for the ecosystem.

Faire is searching for a top-notch staff engineer to lead design and execution as we continue to build our machine learning platform that will power our wholesale marketplace. This role will architect and build scalable, reliable systems to enable seamless software driven machine learning deployment to improve Faire’s core metrics.

What You’ll Do:

Design and build highly scalable machine learning systems that the entire company will use. Examples of these include (but are not limited to)

“Machine-Learning-as-a-service”
Feature store, including batch and real-time feature computation, serving, and monitoring
Deep learning infrastructure that powers Faire’s language and image models
Model prediction services to manage model deployment, inference and monitoring

Partner with our internal customers to understand their ML development pain points and craft platform solutions to address them
Provide technical mentorship to ML engineers and interns on the team
Help shape the long-term strategy and grow the team

What it takes:

5+ years experience building production machine learning systems or platform components such as feature store, model training framework, ML prediction service etc
Degree in a relevant discipline such as Computer Science, Machine Learning, or another similar field
Strong coding skills in Python/Java or equivalent
Experience working within common backend system architectures (e.g. microservices)
Strong understanding of engineering and infrastructure best practices, general software development principles with a machine learning software development life-cycle orientation

Salary Range

San Francisco: the pay range for this role is $224,00 to $308,000 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.

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)

Staff Machine Learning Platform Engineer