NMI is hiring a ML Ops Engineer (USA / Israel)

We're revolutionizing performance marketing by delivering millions of new customer connections to brands monthly. We're seeking a hands-on ML Ops Engineer to collaborate with data scientists in transforming models into production-ready systems. As an MLOps Engineer, you'll report to the Data Science Manager and collaborate closely with Data Scientists and Product developers. You'll design computational infrastructure, strengthen training/inference pipelines, and ensure our machine learning code, data workflows, and services are reliable, reproducible, observable, and cost-effective. You'll also establish best practices and support platform scalability as we expand. This role is preferably located in Israel, but exceptional candidates from the U.S. will be considered. Our Mission: Our objective is to transform how individuals discover and explore new brands through innovative "thank-you" gift introductions. Our customer-centric approach guarantees businesses efficiently acquire new customers while making them feel appreciated and rewarded. We are a data-driven, financially sustainable organization that has experienced 731% growth over three years. Currently, we're expanding to become a premier global customer acquisition platform. Supported by investors who backed successful ventures like Fitbit, Warby Parker, and Twitter, we are positioned for exponential growth and prepared to demonstrate worldwide impact. What you will do: - ML platform: Productionize training and inference, establish CI/CD for models, implement data versioning practices, and model governance - Feature & model lifecycle: Centralize feature generation, manage model registry/metadata, 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 repository structure, testing, logging, and reproducibility - Cross-functional collaboration: Transform prototypes into production systems, provide mentorship and technical guidance - Roadmap & standards: Drive technical vision for ML platform capabilities and establish architectural team standards What you need: - Experience: 5+ years in ML Ops, including large-scale systems infrastructure ownership - Software engineering strength: Strong coding, debugging, performance analysis, testing, CI/CD discipline; reproducible builds; extensive Python pipeline development - Cloud & containers: Production experience with AWS, DataBricks, Docker + Kubernetes - IaC: Terraform or CloudFormation for managed environments - ML tooling: MLflow/SageMaker with production ML pipeline track record - Monitoring/observability: ML monitoring and pipeline alerting - Collaboration: Excellent communication in fast-paced startup environments - PySpark/Glue/Dask/Kafka: Large-scale batch/stream processing experience - Analytics platforms: Third-party data integration expertise - Model serving patterns: Familiarity with real-time endpoints, batch scoring, feature stores - Governance & security: Model governance/compliance understanding - Mission-oriented: Proactive, self-driven with strong initiative What you get: - Competitive compensation, flexible remote work - Unlimited Responsible PTO - Opportunity to join a growing, cash-flow-positive company with direct impact on revenue and future success