ML Ops Engineer

The company 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 the company grows.

Our Mission

The company’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 the company's revenue, growth, scale, and future success