Zendesk

Machine Learning Engineer

98k - 146k EUR98 000 - 146 000 EUR/ rok./ rok.UoPUmowa o pracę (brutto)

MidZdalnie·KrakówFull-time·Umowa o pracę

#448873·Dodano 25 dni temu·41

Źródło: Zendesk

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Tech Stack / Keywords

machine_learning python sql snowflake airflowdbtnlp llma_b_testingcausal_inference

Firma i stanowisko

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.

Wymagania

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.

Obowiązki

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.

Benefity

Hybrid work model with flexibility to work remotely part of the week.
Potential eligibility for bonus, benefits, or related incentives communicated during the offer stage.

Elastyczne godziny

Inne informacje

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.

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