Machine Learning Engineer at Zendesk in Krakow

Job description

Job Description The Enterprise Machine Learning team builds intelligent systems that help the business make better decisions at scale. We combine machine learning, data engineering, and modern AI techniques to transform complex customer and revenue data into products and insights that drive measurable business outcomes. As part of the Enterprise Data & Analytics organization, we work closely with product, engineering, and business stakeholders to build production-ready ML solutions that continuously improve as new data becomes available. Role Overview: As a Machine Learning Engineer , you'll build, deploy, and continuously improve machine learning systems that power this intelligence layer. You'll own the complete lifecycle—from understanding the business problem and developing models to deploying them into production, monitoring performance, and iterating based on real-world results. You'll work alongside AI Engineers, Data Engineers, and Analytics professionals, building scalable ML solutions that become part of our core platform. What You'll Do: 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. This Role Is For You If: You enjoy building machine learning systems that solve real business problems—not just achieving better benchmark metrics. You've worked with imperfect, real-world datasets and understand challenges like noisy labels, feature leakage, class imbalance, and concept drift. You write clean, production-quality Python with testing, modular design, and maintainable code. You think beyond model performance and care about the impact your work has on users and business outcomes. You're comfortable taking ownership of projects from initial idea through production deployment and ongoing maintenance. You use data to guide decisions and proactively identify opportunities for new ML applications. What We're Looking For: 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. Master's or PhD is a plus, but not required. Technical Skills: 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. Experience working with unstructured data using embeddings, fine-tuning, vector representations, or other modern NLP techniques. Strong Python programming skills with experience building production-ready software. Solid SQL skills and experience working with cloud data warehouses (Snowflake preferred). Experience deploying, serving, and monitoring machine learning models in production environments. Familiarity with experiment design, A/B testing, or causal inference is a plus. Experience with workflow orchestration tools such as Airflow, dbt, or similar platforms is a plus. Comfortable using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, o