Senior Staff Machine Learning Platform Engineer

Faire is a technology wholesale platform that uses technology, data, and machine learning to connect independent retailers with products. The Senior Staff Machine Learning Platform Engineer will own the technical vision and evolution of the ML platform, establish organization-wide standards, and lead complex initiatives that improve data science velocity and support scalable machine learning workflows.

Responsibilities:

• Define and drive the long-term architecture of Faire’s ML platform including training, inference, feature management, governance
• Establish company-wide standards for code quality, testing, MLOps (CI/CD), experimentation, model lifecycle management, and observability
• Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns
• Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow
• Optimize performance, reliability, and cost of the ML platform
• Evaluate and integrate emerging Databricks features
• Stay ahead of the curve by engaging with the latest developments in machine learning and AI
• Serve as senior ML technical advisor to Faire’s data science and production engineering teams
• Represent Faire at ML conferences and meetups
• Mentor ML engineers and raise the overall bar for Machine Learning at Faire

Requirements:

• 10-12 years of experience building and improving large-scale ML or data platforms
• A degree
• Computer Science, Engineering, Statistics, or a related technical field
• Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization
• Proven ability to design systems that support multiple data science teams and production workloads
• Strong background in distributed systems, ML infrastructure, and cloud architecture
• Demonstrated technical leadership across teams and orgs; ability to influence without authority
• preferably graduate level
• Experience integrating LLM workflows into enterprise platforms
• Previous contributions to open source ML Infrastructure projects or research publications