# ML Ops Engineer at Nift > Remote · Israel · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Anift%3A5234369007 (read 2026-11-09). ## Facts (observed) - Company: [Nift](https://alderhq.app/companies/nift) - Location text: Remote, Israel - Where it can be done from (inferred): Remote · Israel (evidence: "Remote role open worldwide") - Posted: 2026-09-10 (60 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/nift/jobs/5234369007 - Skills listed (inferred): Python, Kafka, AWS, Kubernetes, Docker, Terraform, Spark ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) 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 & Foundry 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. ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Anift%3A5234369007 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.