Staff Machine Learning Platform Engineer at Faire

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

As a Staff Machine Learning Platform Engineer, you will help design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You are the technical bridge between data science and production engineering. You’ll be joining a small but deeply critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape.

What You Will Do

Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows

Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows

Teach data scientists how to utilize our ML platform to advance development from notebook to production for our most critical models

Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage

Build CI/CD pipelines for ML using Terraform and Git-based workflows (e.g., GitHub Actions)

Optimize performance, reliability, and cost across training and inference workloads

Configure Identity and Access Management (IAM) and Role Based Authentication Controls (RBAC) for sensitive data sets

Establish observability for data quality, model performance, and platform health

Build and maintain ML Platform technical documentation

What it takes

8+ years of experience building production ML or data platforms

A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field

Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.

Proficiency in Python, SQL, and distributed systems concepts

Experience with cloud platforms and infrastructure-as-code

Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security

Experience supporting multiple ML teams in a shared platform environment

Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done

Tech Stack

Faire uses a modern cloud based tech stack. For this role, you’ll want to be proficient with the following:

Category	Technologies
Languages	Python, SQL, Kotlin
ML Frameworks	PyTorch, MLFlow
Big Data & Processing	Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL
Cloud & Infrastructure	AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform
Generative AI	Claude Sonnet 4.5, ChatGPT 5.2

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 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.

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

Job Description