ML Ops Engineer

Nift; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible remote work

Unlimited PTO

Job summary

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re seeking a hands-on ML Ops Engineer to partner with data scientists and turn models into production systems. You’ll architect storage/compute, harden pipelines, and ensure reliability and cost-efficiency.

Reporting to the Data Science Manager, you’ll lead CI/CD, model governance, feature store concepts, and cross-functional collaboration to scale our ML platform across the organization.

Qualifications

• 5+ years in ML Ops with ownership of large-scale ML infra.
• Strong Python coding, debugging, testing, and CI/CD discipline.
• Experience with AWS and containerized environments.
• Experience with ML tooling and model governance.
• Excellent collaboration with data scientists and engineers.
• Experience with large-scale batch/stream processing (PySpark/Kafka).

Responsibilities

• ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models and data/versioning practices.
• Feature & model lifecycle: Centralize feature generation and manage model registry/metadata.
• Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health.
• Engineering excellence: Refactor research code into reusable components, ensure repo structure and testing.
• Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship.
• Roadmap & standards: Drive technical vision for ML platform capabilities and set architectural patterns.

Skills

ML Ops

Python

CI/CD

AWS

Docker

Kubernetes

Terraform

Databricks

SageMaker

MLflow

Feature stores

Model governance

PySpark

Kafka

Data versioning

Collaboration

Tools

Databricks

SageMaker

Git

Airflow

Job description

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.

In this role, 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.

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 Spark Capital & Foundry who also invested in Slack, Snap, SeatGeek, Fitbit, Warby Parker, Wayfair 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