- 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. Before you apply, give Zip a try -> rebrand.ly/check-zip-out

Role Description As an ML Ops Engineer, you will design, build, and operate the infrastructure and tooling that power end-to-end ML workflows on AWS, including SageMaker, Bedrock, and Snowflake Cortex. You will partner closely with data scientists, ML engineers, and platform teams to ensure models are reliable, secure, and scalable in production. Key Responsibilities - Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code. - Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring. - Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake. - Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services. - Automate environment provisioning, configuration, and dependency management across dev, test, and production. - Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA). - Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services. - Contribute to best practices, standards, and documentation for ML platform and operations across the organization. Qualifications - 8+ years of experience in software engineering, data engineering, or ML engineering with at least 4+ years focused on MLOps or ML platform work. - Strong proficiency with Python and experience integrating ML libraries or frameworks (e.g., scikit-learn, TensorFlow, PyTorch) into production workflows. - Hands-on expertise with AWS services relevant to MLOps: SageMaker, Bedrock, IAM, CloudWatch, ECR, ECS/EKS or Lambda, S3, Step Functions, and Glue. - Experience with Snowflake (including Snowflake Cortex), SQL, and building secure, performant data pipelines into and out of Snowflake. - Proficiency with CI/CD tools (e.g., GitHub Actions, GitLab CI, CodePipeline) and infrastructure-as-code (e.g., Terraform, CloudFormation, CDK). - Familiarity with containerization and orchestration (Docker, Kubernetes) and event streaming tools (e.g., Kafka) is a plus. - Knowledge of software engineering best practices, including testing, code reviews, version control, and design for reliability and scalability. - Experience in regulated domains or with healthcare data standards and regulations is a plus (e.g., HIPAA, FHIR, HL7). Education - Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics, or Information Technology. Benefits - Competitive salary and benefits package. - Performance-based incentives may be available. - Health insurance, life insurance, company-paid disability, 401k. - 18+ days of paid time off. Base Compensation Range $151,800 — $202,000 USD Equal Opportunity Statement Inovalon is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status.

• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs • Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification • Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows • Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects • Document and optimize computational workflows following reproducible research best practices • Present findings through technical reports, visualizations, and presentations to cross-functional teams

• With your technical expertise you will accompany the entire development process: from requirements gathering through architecture and implementation to production operation. • In interdisciplinary teams with clients, colleagues and partners you will develop well-founded and sustainable solution approaches. • Regular training and expanding your own skills, including beyond your core area, are part of your daily work. • You will design and implement scalable Data-Lakehouse solutions (e.g. on Databricks) in close collaboration with business and IT stakeholders. • You select appropriate approaches: from classical Machine/Deep Learning to Large Language Models (LLMs) and agent systems, and integrate models into existing data and application landscapes. • You plan and implement data platforms and AI workloads in private and, where applicable, hybrid cloud environments, taking data privacy, compliance and regulatory requirements into account.