Description
The Enterprise Machine Learning team at Zendesk builds intelligent systems that help the business make better decisions at scale.
As a Machine Learning Engineer, you'll build, deploy, and continuously improve machine learning systems that power this intelligence layer.
Responsibilities:
- Design, build, and deploy machine learning models that predict key business outcomes such as churn, expansion, conversion, and customer engagement.
- Develop and maintain scalable ML pipelines that continuously ingest data, retrain models, validate performance, and serve predictions in production.
- Work with large-scale structured and unstructured data, applying modern ML and LLM techniques where they deliver measurable business value.
- Monitor production models, improve performance over time, and ensure reliability through testing, validation, and continuous iteration.
- Collaborate with Data Engineers, Product Managers, and business stakeholders to deliver ML solutions that solve real customer and business problems.
- Own machine learning solutions end-to-end,from problem definition and experimentation to production deployment, monitoring, and ongoing optimization.
Requirements:
- 3–5 years of professional experience in Machine Learning Engineering, Applied Machine Learning, Data Science, or a closely related field.
- Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or another quantitative discipline.
- Strong understanding of machine learning and statistical modeling techniques, including regression, classification, survival analysis, causal inference, uplift modeling, or related methods.
- Experience developing models on large-scale, real-world datasets, including feature engineering, model validation, and handling imperfect data.
- Strong Python programming skills with experience building production-ready software.
- Solid SQL skills and experience working with cloud data warehouses (Snowflake preferred).
Nice to Have:
- Experience deploying, serving, and monitoring machine learning models in production environments.
- Familiarity with experiment design, A/B testing, or causal inference.
- Experience with workflow orchestration tools such as Airflow, dbt, or similar platforms.
- Comfortable using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or similar.
Benefits:
- Salary: €98,000.00-€146,000.00 per year
- Hybrid work environment
- Opportunity for bonus and benefits