Senior Machine Learning Engineer

Deep expertise in building and operating production-grade ML and data platforms using Spark (PySpark + SQL), Databricks (Azure), and Delta Lake, with strong hands-on experience in MLOps practices including MLflow model lifecycle management, feature store architecture (offline + online), CI/CD for ML workflows, and scalable model deployment. Proven ability to design reliable, cost-efficient distributed data systems, optimize Spark workloads, and implement robust governance, observability, and access controls across ML data pipelines. Strong cloud engineering fundamentals in Azure, including orchestration, infrastructure reliability, and integration with services such as CosmosDB and downstream analytics systems.
The Data Engineering and Machine Learning teams at Zip exist to make data and ML production-ready, trusted, and scalable across the business. Our mission is to elevate the quality, reliability, and accessibility of data assets while enabling innovative AI-driven applications that create measurable customer and commercial impact.
We operate with an ownership mindset — engineers here don’t just build pipelines, they own platforms end-to-end. Great talent on this team thrives in ambiguity, designs with scale and reliability in mind, and proactively improves standards rather than maintaining the status quo. We work collaboratively across Data Science, Analytics, and Engineering, balancing speed with engineering discipline. We value pragmatic problem-solvers who think in systems, prioritize observability and maintainability, and are motivated by building infrastructure that empowers others to move faster and smarter.

Start your adventure with Zip
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.
Our goals include enhancing the discipline within our data engineering practices, strengthening our collaboration with the Data Analytics and Data Science teams, and elevating the quality of our data assets. These changes are designed to better position us to leverage the full potential of our data, allowing us to explore new and innovative applications, including the use of AI.
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

What you’ll bring to the team
Experience

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)

MLOps & ML Systems

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

Cloud & Platform Experience

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

What you’ll get in return
Zip is a place where you’ll get out what you put in. The newness of our sector means we need to move at pace and embrace change, and our promise to you when you join the team is that you’ll feel empowered and trusted to make big things happen quickly.
We want you to feel welcome and as though you have the support to be yourself, and care for yourself at work. Because it’s important to us that you make the most of the opportunities you’ll get to grow your skills and your career, and be surrounded by smart, friendly people and leaders that have your back.
We think these are just some of the best things about being a Zipster. We will also offer you:

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

#LI-Remote
Zip is committed to a straightforward and transparent pay structure. The actual base salary will be determined by various individualized factors, including job-related knowledge, skills, experience, location, internal equity, as well as other objective business considerations.
The annual base Pay Range for this position is $150,000- 161,000K. This range reflects our US national compensation band (USN). Additional premium percentages may apply based on our tiered premium strategy.
Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or commission awards, in addition to a full range of medical, financial, and/or other benefits.
If hired, employees will be in an 'at-will position' and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation or benefit program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
Be a part of a team that reflects the diversity of our customers
We pride ourselves on being a workplace that provides equal opportunities to people of all ages, cultural backgrounds, sexual orientations, gender identities, abilities, veteran status, and everything else that makes you unique.
Equally, we’re committed to ensuring our recruitment processes are accessible and inclusive. Please let us know If there are any adjustments that need to be made to ensure you have a fair and equitable experience.
And finally…get to know us
Zip Co Limited (ASX: ZIP) is a digital financial services company, offering innovative, people-centered products.
Operating in two core markets - Australia and New Zealand (ANZ) and the US, Zip offers access to point-of-sale credit and digital payment services, connecting millions of customers with its global network of tens of thousands of merchants. We’re proud to be a values-led business and our values - Customer First, Own it, Stronger Together and Change the Game - guide us in everything we do.
I acknowledge by clicking "Submit Application", that the information provided is true and correct. I also understand that any willful dishonesty may render for refusal of this application or immediate termination of employment. By providing your information, you acknowledge that you have read our Zip Applicant and Candidate Privacy Notice and authorize Zip to process your data subject to those terms. Zip participates in the federal government’s E-Verify program.
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