Machine Learning Engineer

Zendesk software powers billions of conversations and is designed to improve customer service experience. The Enterprise Machine Learning team is part of the Enterprise Data & Analytics organization, focusing on building intelligent ML systems to help the business make better decisions and transform complex data into impactful insights.

• 3–5 years of professional experience in Machine Learning Engineering, Applied Machine Learning, Data Science, or related field.
• Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or quantitative discipline; Master’s or PhD is a plus.
• Strong understanding of machine learning and statistical modeling techniques including regression, classification, survival analysis, causal inference, uplift modeling.
• Experience with large-scale real-world datasets, feature engineering, model validation, and handling imperfect data.
• Experience with unstructured data and modern NLP techniques like embeddings and fine-tuning.
• Proficient in production-quality Python programming with testing and modular design.
• Solid SQL skills and experience with cloud data warehouses such as Snowflake.
• Experience deploying, serving, and monitoring ML models in production.
• Familiarity with experiment design, A/B testing, or causal inference is a plus.
• Experience with workflow orchestration tools like Airflow, dbt, or similar is a plus.
• Comfortable using AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot.

• Design, build, and deploy machine learning models predicting key business outcomes such as churn, expansion, conversion, and customer engagement.
• Develop and maintain scalable ML pipelines for continuous data ingestion, model retraining, performance validation, and prediction serving.
• Work with large-scale structured and unstructured data, applying modern ML and LLM techniques.
• Monitor production models to improve performance, ensure reliability through testing, validation, and continuous iteration.
• Collaborate with Data Engineers, Product Managers, and business stakeholders to deliver ML solutions addressing real customer and business problems.
• Own machine learning solutions end-to-end from problem definition, experimentation, deployment, monitoring, to ongoing optimization.

• Artificial intelligence or automated decision systems may be used to screen or evaluate applications.
• Equal opportunity employer with commitment to diversity, equity, and inclusion.
• Provides reasonable accommodations for applicants with disabilities and disabled veterans.