Senior Machine Learning Engineer

Zip Co is a digital financial services company offering innovative products, and they are seeking a Senior Machine Learning Engineer to build and scale the systems that enable production-grade machine learning and AI. The role focuses on managing the ML lifecycle, collaborating with data science and engineering teams, and solving complex distributed systems problems.

Responsibilities:

• Own and scale the infrastructure that powers production ML and AI across Zip
• Build and maintain batch and streaming feature pipelines
• Design and manage offline and online feature store patterns
• Define MLflow model registry standards and promotion workflows
• Deploy and operate scalable model serving endpoints
• Implement CI/CD for ML pipelines and model deployment
• Develop pipelines using PySpark and Spark SQL
• Optimize joins, partitioning, and shuffle-heavy workloads
• Improve reliability and cost-efficiency of distributed data jobs
• Support streaming workloads using Delta Live Tables
• Manage Databricks clusters, jobs, and access controls
• Improve observability, alerting, and operational standards
• Contribute to Lakehouse architecture (Databricks and Snowflake)
• Implement governance, RBAC, and data quality standards
• Build infrastructure that accelerates experimentation and model deployment
• Support emerging AI use cases, including real-time and large-scale ML systems

Requirements:

• 8+ years of experience in Machine Learning with a strong focus on production-grade ML and distributed data systems
• Demonstrated experience owning and operating ML systems end-to-end in production environments
• Advanced experience with PySpark and Spark SQL
• Strong understanding of Spark execution (joins, shuffles, partitioning)
• Experience building and optimizing reliable, scalable data pipelines
• Strong data engineering fundamentals including medallion architecture design, incremental/idempotent ETL patterns, and Delta Lake optimization (partitioning)
• Experience operating ML systems in production
• Hands-on experience with MLflow (tracking + model registry)
• Experience managing feature stores (offline + online)
• Experience deploying and monitoring model serving endpoints
• Experience implementing CI/CD for ML workflows
• Experience working in Azure
• Production experience with Databricks and Delta Lake
• Experience integrating with CosmosDB or similar NoSQL key-value stores
• Experience designing orchestrated, production-grade data workflows (Databricks Workflows, Airflow, or ADF) with dependency management, backfills, and failure recovery
• Delta Live Tables and streaming pipelines
• Iceberg or Lakehouse Federation experience
• Snowflake experience
• Vector databases or LLM infrastructure
• Infrastructure-as-code experience