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Nift is a performance marketing platform delivering millions of new customers to brands monthly. The company is cash-flow-positive, backed by Spark Capital and Foundry (investors in Slack, Snap, Fitbit, Warby Parker, Twitter), and has achieved 731% growth over three years.
You will report to the Data Science Manager and partner closely with data scientists and product developers to operationalize machine learning at scale. Your core responsibilities include:
**ML Platform & Infrastructure**: Productionize training and inference pipelines (batch and real-time), establish CI/CD for models, implement data and model versioning practices, and enforce model governance frameworks.
**Feature & Model Lifecycle Management**: Centralize feature generation using feature store patterns, manage model registry and metadata, and streamline deployment workflows to reduce time-to-production.
**Observability & Quality Assurance**: Implement comprehensive monitoring for data quality, model drift, performance/latency metrics, and pipeline health with clear alerting and dashboards.
**Engineering Excellence**: Refactor research code into reusable, maintainable components; enforce repository structure, testing standards, logging practices, and reproducibility across the team.
**Cross-Functional Collaboration**: Work with data scientists, analytics, and engineering teams to transform prototypes into production systems; provide mentorship and technical guidance to elevate team capabilities.
**Platform Vision & Standards**: Drive the technical roadmap for ML platform capabilities and establish architectural patterns that become organizational standards.
**Requirements:**
- 5+ years of ML Ops experience with demonstrated ownership of ML infrastructure for large-scale systems
- Strong software engineering fundamentals: coding, debugging, performance analysis, testing, CI/CD discipline, and reproducible builds
- Extensive commercial Python experience developing automated pipelines that bring ML models to production
- Production experience with AWS, Databricks, Docker, and Kubernetes (EKS/ECS or equivalent)
- Infrastructure-as-Code proficiency with Terraform or CloudFormation
- Hands-on experience with ML tooling (MLflow, SageMaker, or similar) and production ML pipelines
- Expertise in ML monitoring (quality, drift, performance) and pipeline alerting
- Experience with large-scale batch/stream processing (PySpark, Glue, Dask, Kafka)
- Familiarity with model serving patterns: real-time endpoints, batch scoring, and feature stores
- Exposure to model governance, compliance, and secure ML operations
- Experience integrating third-party data and analytics platforms
- Excellent communication skills and comfort working with data scientists, analysts, and engineers in a fast-paced startup environment
- Mission-oriented mindset: proactive, self-driven, takes ownership, and goes beyond expectations
