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NNift
NML Ops Engineer (USA / Israel)
JOB SUMMARY
Job details
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.
As a MLOps Engineer, 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.
This role is ideally based in Israel, but strong candidates from the U. S. will also be considered.
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 investors who supported Fitbit, Warby Parker, and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growthNift-Ranked-Number-120-Fastest-Growing-Company-in-North-America-on-the-2025-Deloitte-Technology-Fast-500" rel="nofollow ugc noopener noreferrer" target="_blank"> here.
What you will do:ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governanceFeature model lifecycle: Centralize feature generation (e. g. , feature store patterns), manage model registry/metadata, and streamline deployment workflowsObservability quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboardsEngineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibilityCross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidanceRoadmap standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standardsWhat you need:Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systemsSoftware 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 environmentsML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelinesMonitoring/observability: ML monitoring (quality, drift, performance) and pipeline alertingCollaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startupPySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processingAnalytics platforms: Experience integrating 3rd party dataModel serving patterns: Familiarity with real-time endpoints, batch scoring, and feature storesGovernance security: Exposure to model governance/compliance and secure ML operationsBe 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 doneWhat you get: Competitive compensation, flexible remote work U. S Only: (401(k) with 4% match,
benefits
like Medical/Dental/Vision)Unlimited Responsible PTOGreat opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success.
We’re looking for a hands-on ML Ops Engineer to partner with our data scientists to turn their models into production-ready systems.
As a MLOps Engineer, 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.
This role is ideally based in Israel, but strong candidates from the U. S. will also be considered.
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 investors who supported Fitbit, Warby Parker, and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growthNift-Ranked-Number-120-Fastest-Growing-Company-in-North-America-on-the-2025-Deloitte-Technology-Fast-500" rel="nofollow ugc noopener noreferrer" target="_blank"> here.
What you will do:ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governanceFeature model lifecycle: Centralize feature generation (e. g. , feature store patterns), manage model registry/metadata, and streamline deployment workflowsObservability quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboardsEngineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibilityCross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidanceRoadmap standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standardsWhat you need:Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systemsSoftware 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 environmentsML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelinesMonitoring/observability: ML monitoring (quality, drift, performance) and pipeline alertingCollaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startupPySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processingAnalytics platforms: Experience integrating 3rd party dataModel serving patterns: Familiarity with real-time endpoints, batch scoring, and feature storesGovernance security: Exposure to model governance/compliance and secure ML operationsBe 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 doneWhat you get: Competitive compensation, flexible remote work U. S Only: (401(k) with 4% match,
benefits
like Medical/Dental/Vision)Unlimited Responsible PTOGreat opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success.
The company
N
Nift United States
