About this role
Engineers who thrive in fast-paced startup environments and enjoy taking full ownership of infrastructure will find a strong fit at Nift. The engineering group partners directly with data scientists to scale performance marketing systems that deliver millions of new customer acquisitions monthly. Daily responsibilities center on architecting compute storage, hardening training and inference pipelines, and ensuring machine learning services remain reliable, reproducible, and cost-efficient. Nift operates as a cash-flow-positive company backed by prominent venture capital firms and is currently expanding its global platform. Candidates who excel here combine robust software development skills with a practical approach to operationalizing machine learning models.
What you will do
- Productionize batch and real-time training and inference while establishing CI/CD practices for models.
- Centralize feature generation patterns and manage the model registry and metadata workflows.
- Implement monitoring systems for data quality, drift, model performance, latency, and pipeline health.
- Refactor research code into reusable components with strict repository structure, testing, and logging.
- Collaborate with data science, analytics, and engineering teams to transform prototypes into production systems.
- Drive the technical vision for machine learning platform capabilities and establish team architectural standards.
What you need
- Bring over five years of experience in ML Ops, specifically owning machine learning infrastructure for large-scale systems.
- Demonstrate strong software engineering capabilities in coding, debugging, performance analysis, and automated testing.
- Possess extensive commercial experience using Python to build automated pipelines that bring models to production.
- Show production experience working on AWS, Databricks, Docker, and Kubernetes or equivalent container platforms.
- Use Infrastructure as Code tools such as Terraform or CloudFormation for managed environments.
- Apply familiarity with machine learning tooling like MLflow or SageMaker along with monitoring for data drift and quality.
Nice to have
- Experience with PySpark, Glue, Dask, or Kafka for large-scale batch and stream processing
- Experience integrating third-party data analytics platforms
- Familiarity with real-time endpoints, batch scoring, and feature stores
- Exposure to model governance, compliance, and secure machine learning operations
Benefits
- Competitive compensation
- Flexible remote work
- Unlimited Responsible PTO
Skills and tech stack
Questions candidates ask
What is the remote work policy?
The role offers flexible remote work based out of Israel. Candidates will collaborate with team members in a fast-paced startup environment.
What compensation and time off are provided?
The position includes competitive compensation and unlimited responsible PTO. Employees also join a growing, cash-flow-positive company.
What technologies will I work with?
The tech stack includes Python, AWS, Databricks, Docker, Kubernetes, Terraform, MLflow, and SageMaker. You will also use data processing tools like PySpark, Glue, Dask, and Kafka.
Who will I be working with?
You will report to the Data Science Manager and partner closely with data scientists, product developers, and analytics engineers. The role involves turning research prototypes into reliable production systems.