Staff Machine Learning Platform Engineer

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Company Overview

A technology wholesale platform is helping independent retailers around the world discover products and connect with suppliers through technology, data, and machine learning. The platform supports a global community of entrepreneurs and aims to provide retailers with the tools and insights needed to grow their businesses and strengthen local communities.

The organization is seeking a Staff Machine Learning Platform Engineer to design, improve, and operate a scalable machine learning platform that accelerates model training, deployment, and governance. This role serves as a technical bridge between data science and production engineering.

The successful candidate will join a specialized engineering team responsible for expanding the organization’s ability to support machine learning initiatives at scale. The role focuses on ML infrastructure, MLOps, data governance, automation, observability, security, and platform reliability.

Key Responsibilities

• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows.
• Productionize machine learning workloads using Spark, Delta Lake, MLflow, and Databricks Workflows.
• Enable data scientists to effectively use the ML platform and advance models from notebooks into production.
• Support production deployment of critical machine learning models.
• Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage.
• Build and maintain CI/CD pipelines for machine learning using Terraform and Git-based workflows such as GitHub Actions.
• Optimize performance, reliability, and infrastructure costs across model training and inference workloads.
• Configure Identity and Access Management (IAM) and Role-Based Access Control (RBAC) for sensitive datasets.
• Establish observability for data quality, model performance, and overall platform health.
• Develop and maintain comprehensive technical documentation for the ML platform.
• Collaborate with data scientists, engineers, and other technical teams to improve machine learning development and production workflows.
• Take ownership of complex or unresolved platform challenges and acquire the necessary knowledge to address them effectively.

Required Qualifications

• 8+ years of experience building production machine learning or data platforms.
• Degree in Computer Science, Engineering, Statistics, or a related technical field, preferably at the graduate level.
• Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.
• Proficiency in Python and SQL.
• Strong understanding of distributed systems concepts.
• Experience working with cloud platforms and infrastructure-as-code technologies.
• Strong understanding of MLOps best practices, including CI/CD, monitoring, reproducibility, and security.
• Experience supporting multiple machine learning teams within a shared platform environment.
• Strong understanding of data governance, access control, observability, and secure machine learning infrastructure.
• Ability to take ownership of complex problems and develop the knowledge required to deliver effective solutions.
• Strong collaboration and communication skills when working across data science, engineering, and platform teams.

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