Remote ML Platform Engineer — Scale Production AI

Faire; San Francisco (CA); On-site; USD 247,000 - 339,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Hybrid work model

Job summary

Faire is seeking a Staff Machine Learning Platform Engineer to design, improve, and operate a scalable ML platform that accelerates model training, deployment, and governance. You will bridge data science and production engineering, joining a small but critical team supporting thousands of local businesses.

The role requires expertise with Databricks, Spark, Delta Lake, MLflow, Python and SQL, cloud/infrastructure-as-code, and strong MLOps practices.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

SQL

Distributed systems

MLOps

Cloud platforms

CI/CD

Education

Bachelor’s degree in CS/Engineering/Statistics

Tools

Databricks

Spark

Delta Lake

MLflow

Unity Catalog

Terraform

Kubernetes

Airflow

Job description

Faire is seeking a Staff Machine Learning Platform Engineer to design, improve, and operate a scalable ML platform that accelerates model training, deployment, and governance. You will bridge data science and production engineering, joining a small but critical team supporting thousands of local businesses.

The role requires expertise with Databricks, Spark, Delta Lake, MLflow, Python and SQL, cloud/infrastructure-as-code, and strong MLOps practices.