[Hiring] Senior Machine Learning Engineer @Zip Co Limited

Role Description

We’re hiring a Senior Machine Learning Platform Engineer to build and operate the infrastructure that powers production-grade machine learning at Zip. In this role, you’ll own the ML lifecycle end-to-end — from feature pipelines and model registry standards to CI/CD and scalable model serving on Databricks (Azure). You’ll ensure our ML systems are reliable, observable, and built to scale as we expand AI-driven capabilities across the business.

Interesting problems you’ll get to solve:

• Own the ML Lifecycle (MLOps)
• Build and maintain feature pipelines (batch + streaming)
• Manage offline and online feature store patterns (CosmosDB-backed online lookup)
• Administer and enforce standards around MLflow model registry, versioning, and promotion workflows
• Deploy and operate model serving endpoints
• Implement CI/CD for ML pipelines and model deployment
• Participate in on-call rotation for platform-owned systems
• Build Production-Grade Spark Systems
• Develop pipelines using PySpark and Spark SQL
• Optimize joins, partitioning, and shuffle-heavy workloads
• Improve reliability and cost-efficiency of Spark jobs
• Ensure pipelines are modular, testable, and production-ready
• Support streaming workloads using Delta Live Tables
• Operate and Improve the Platform
• Administer Databricks clusters, jobs, policies, and permissions
• Improve observability, alerting, and operational standards
• Contribute to Lakehouse Federation initiatives (Databricks ↔ Snowflake via Iceberg)
• Integrate ML services into downstream architecture
• Implement governance, access controls (RBAC), and data quality/observability standards across ML data pipelines

Qualifications

• 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
• Strong Spark Capability (Core Requirement)
• 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

Nice to Have

• Delta Live Tables and streaming pipelines
• Iceberg or Lakehouse Federation experience
• Snowflake experience
• Vector databases or LLM infrastructure
• Infrastructure-as-code experience

Benefits

• Flexible working culture
• Incentive programs
• Unlimited PTO
• Generous paid parental leave
• Leading family support policies
• Company-sponsored 401k match
• Learning and wellness subscription stipend
• Beautiful Union Square office with a casual dress code
• Industry-leading, employer-sponsored insurance for you and your dependents, with several 100% Zip-covered choices available