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 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
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