About The Role

Join Faire as a Staff Machine Learning Platform Engineer, where you'll design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You'll be the technical bridge between data science and production engineering, working to support tens of thousands of local businesses in a challenging retail landscape. Your responsibilities will include designing and operating ML infrastructure, productionizing ML workloads, teaching data scientists how to utilize the ML platform, implementing data governance, building CI/CD pipelines, optimizing performance, and establishing observability for data quality and model performance.

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.
Faire uses a modern cloud based tech stack. For this role, you’ll want to be proficient with the following:
Python, SQL, Kotlin
PyTorch, MLFlow
Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL
AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform
Claude Sonnet 4.5, ChatGPT 5.2
Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow
Experience supporting multiple ML teams in a shared platform environment
Proficiency in Python, SQL, and distributed systems concepts
Experience with cloud platforms and infrastructure-as-code
8+ years of experience building production ML or data platforms
Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done
A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security

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