ML Ops Engineer

Job description

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re looking for a hands-on **ML Ops Engineer** to partner with our data scientists to turn their models into production-ready systems. In this role, you’ll report to the Data Science Manager and work closely with our Data Scientists and Product developers. You’ll architect storage and compute, harden training/inference pipelines, and make our ML code, data workflows, and services reliable, reproducible, observable, and cost-efficient. You’ll also set best practices and help scale our platform as Nift grows. **Our Mission:** Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by [Spark Capital](https://www.sparkcapital.com/) & [Foundry](https://foundry.vc/) who also invested in Slack, Snap, SeatGeek, Fitbit, Warby Parker, Wayfair and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth [here](https://www.businesswire.com/news/home/20251119582928/en/Nift-Ranked-Number-120-Fastest-Growing-Company-in-North-America-on-the-2025-Deloitte-Technology-Fast-500). **What you will do:** - ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance - Feature & model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows - Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards - Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility - Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance - Roadmap & standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standards **What you need:** - Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systems - Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds. Extensive commercial experience with Python developing automated pipelines bringing ML models to production - Cloud & containers: Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent) - IaC: Terraform or CloudFormation for managed, reviewable environments - ML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelines - Monitoring/observability: ML monitoring (quality, drift, performance) and pipeline alerting - Collaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup - PySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processing - Analytics platforms: Experience integrating 3rd party data - Model serving patterns: Familiarity with real-time endpoints, batch scoring, and feature stores - Governance & security: Exposure to model governance/compliance and secure ML operations - Be mission-oriented: Proactive and self-driven with a strong sense of initiative; takes ownership, goes beyond expectations, and does what's needed to get the job done **What you get:** - Competitive compensation, flexible remote work - Unlimited Responsible PTO - Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success