Production ML Engineer - Scale Real-World Models (Hybrid)

Zendesk, Inc.; United States; Hybrid; USD 170,000 - 230,000; Full time

Job summary

Zendesk, Inc. is seeking a Machine Learning Engineer to design, build, and deploy production ML systems that power intelligent features for our platform. You will develop scalable pipelines, train models on large datasets, and monitor performance in production to ensure reliability and impact.

You will work with cross-functional teams to translate business problems into ML solutions. A strong background in Python, NLP, and production-grade software is essential; hybrid work is available across

Qualifications

• Bachelor's degree in a quantitative field required.
• Masters/PhD is a plus.
• Strong ML and statistical modeling knowledge.
• Experience with large-scale datasets and feature engineering.
• Experience with embeddings and NLP techniques.
• Production-ready Python development experience.
• Cloud data warehouses experience (Snowflake preferred).
• Experience deploying and monitoring models in production.

Responsibilities

• Design, build, and deploy ML models predicting churn, expansion, conversion, and engagement.
• Develop scalable ML pipelines that ingest data, retrain models, validate performance, and serve predictions.
• Monitor production models and iterate to improve performance over time.
• Collaborate with Data Engineers, Product Managers, and stakeholders to deliver ML solutions.
• Own machine learning solutions end-to-end—from problem definition to production deployment.

Skills

Python

SQL

ML Engineering

NLP / Embeddings

Experiment Design

A/B Testing

Model Deployment

Education

Bachelor's degree in a quantitative field

Master's or PhD (bonus)

Tools

Snowflake

Airflow

dbt

GitHub Copilot

Claude Code

Cursor

Job description

Zendesk, Inc. is seeking a Machine Learning Engineer to design, build, and deploy production ML systems that power intelligent features for our platform. You will develop scalable pipelines, train models on large datasets, and monitor performance in production to ensure reliability and impact.

You will work with cross-functional teams to translate business problems into ML solutions. A strong background in Python, NLP, and production-grade software is essential; hybrid work is available across