Machine Learning Engineer

About

powers over 3 million websites across 20+ data centers worldwide — helping creators, entrepreneurs, and businesses build their online presence with confidence.

We're growing fast and evolving how we serve our global customers. The next phase of our journey is fueled by data: transforming how we understand, engage, and support millions of users through smarter, more personalized experiences.

About the Role

We're looking for a Machine Learning Engineer who combines data engineering excellence with applied machine learning expertise to help us build our Customer Success Engine — a system that predicts, segments, and activates customer insights in real time.

Your work will directly power how we engage with customers, reduce churn, and deliver personalized experiences across our tools and platforms. From developing ML models to automating feature pipelines, this role bridges data, engineering, and operational enablement at scale.

What You'll Do

Modeling & Feature Engineering

• Build and optimize customer segmentation, churn, and propensity models using modern ML frameworks.

• Design and maintain feature pipelines in Snowflake using SQL, dbt, and Snowpark.

• Develop reproducible training, evaluation, and scoring workflows with automated monitoring and version control.

• Partner closely with Analysts and business stakeholders to ensure models are aligned with customer and business logic.

Automation & Model Operations

• Create automated pipelines to evaluate model performance, detect drift, and refresh scores.

• Build and deploy scoring systems that update customer audiences or retention scores in real time.

• Integrate model outputs into systems like Braze, support tools, and CRM platforms.

• Ensure activation workflows are reliable, monitored, and fully auditable.

Data Engineering & Infrastructure

• Implement and maintain scalable data models, transformation logic, and feature tables in Snowflake.

• Collaborate with Data Engineers to align model workflows within the broader data architecture.

• Apply CI/CD, validation, and observability practices to ensure production-grade reliability.

• Document model logic, features, and lineage to enable operational transparency and reuse.

What You'll Bring

• Degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.

• Proven experience as a Machine Learning Engineer, ideally in production-grade environments.

• Expertise in SQL (preferably Snowflake) and Python (pandas, scikit-learn, Snowpark, or equivalent).

• Experience deploying, monitoring, and maintaining ML models in production.

• Familiarity with dbt, Git, CI/CD pipelines, and MLOps frameworks (e.g., MLflow).

• Strong analytical mindset, capable of translating technical work into measurable business outcomes.

• Excellent communication skills and comfort working across technical and commercial teams.

Nice to Have

• Background in subscription analytics or digital customer behavior modeling.

• Knowledge of A/B testing, uplift modeling, or causal inference.

• Familiarity with feature stores, model monitoring, or drift detection frameworks.

Why Join Us

• Be part of building the Customer Success Engine — a core data-driven initiative shaping the future of

• Work with a modern data stack: Snowflake, dbt, Python, and Tableau.

• Collaborate with a global, cross-functional team that values innovation and technical ownership.

• Enjoy full remote flexibility or work from one of our international hubs.

• Thrive in a culture built on curiosity, ownership, and continuous learning.

Ready to Build What's Next?

If you're excited by data that drives real-world impact — and you love turning machine learning models into operational success stories — we'd love to meet you.

 Apply now and help us power the next generation of intelligent customer engagement