Nift Networks
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 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
#J-18808-Ljbffr
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 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
#J-18808-Ljbffr