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# Machine learning engineer

- **Company:** Just Eat Takeaway.com
- **Location:** Diemen, Netherlands
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Continuous Integration, Information Engineering, Software Debugging, Software Design Patterns, Distributed Computing Environment, Python (Programming Language), Machine Learning, NumPy, Object-Oriented Software Development, Tensorflow, Azure Machine Learning, Software Deployment, Software Engineering, SQL Databases, Data Streaming, Feature Store, Feature Engineering, Google Vertex AI, Pandas, Event Driven Architecture, Scikit Learn, Low Latency, Data Analytics, Machine Learning Operations, Restful APIs, Software Version Control, Docker
- **Published:** November 9, 2026
- **Apply:** https://nl.talent.com/redirect?id=644641554692972519&source=vacant&utm_medium=xml&utm_campaign=arbitrage&utm_product=vacantnewsponso&puid=adaaaddcdddc9ddabdacaddfadabaddbgddc9ddaddd79dde9dd79dd9bddeaddbbdafadd7ced39ddebdabaed2fddcadd7dddgaddgfddgaddeaed39cdb6bdbgb9ebe933def3de93de73ded3de73de8gddegddffdde3dea3ee3ddd9eddgaed33ee33dec3deg3ee33ee33ee33ee33ee33ee33ee33e&pos=3&xml_batch_generation_id=d9f20b57-6110-44c0-9107-07959e947ccc&falcon=1&pid=bad470e5876c&bpid=12ab80b811fa8dc0355d3e73c8b88188

## About the Role

* Strong experience in machine learning, specifically developing ML infrastructure platforms for consumer pricing and policy-related products.
 * Hands-on experience developing data science models (predicting and causal modelling) using Python libraries such as pandas, NumPy, scikit-learn, and TensorFlow.
 * Proficiency in Python and a strong understanding of software engineering principles (object-oriented design, design patterns, testing) applied to ML.
 * Demonstrable experience designing and operating ML systems in production (beyond notebook-based training), including familiarity with serving patterns such as REST APIs, batch inference, and event-driven architectures.
 * Ability to work independently and solve complex problems, with a proven track record of productising and deploying models using cloud-native ML platforms (e.g., AWS SageMaker, GCP Vertex AI).
 * Familiarity with orchestration, tracking, and containerisation tools, particularly Airflow, MLflow, and Docker.
 * Solid Understanding of data engineering fundamentals (e.g., SQL, distributed data processing) to debug and optimise data flows.

## Description

We are looking for a Machine Learning Engineer with passion for using machine learning to create intelligent applications. As a Machine Learning Engineer, you will be part of the Pricing Data Team inside the User Behavior Data Team under the umbrella of Data and Analytics pillar with 500+ data engineers, analysts, scientists.

You will collaboratively design, build and will focus on evolving existing machine learning and AI capabilities across the platform, improving those capabilities, and innovating new ones for the future. As a member of the Pricing Data Team, you will develop solutions to estimate demand price elasticity and generate counterfactuals through simulation, finally optimize consumer pricing policy to ensure a reliable, efficient, and revenue generated service.
These are some of the key components to the position:

 * Design, develop, and train data science models to solve complex business problems, taking full end-to-end ownership from initial prototyping to production deployment.
 * Strong knowledge of regression, boosting gradient, causal inference data science models, feature engineering and evaluation of model performance.
 * Develop the architectural design of ML systems, building both the core algorithmic training pipelines and the scalable inference and monitoring infrastructure.
 * Collaborate extensively along with other Data Scientists, Data Engineers, and Product stakeholders to bridge the gap between model development and production systems.
 * Drive the adoption of MLOps best practices (e.g., CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists.
 * Communicate the complexities of ML systems (e.g.,e.g., algorithmic choices, latency vs. accuracy trade-offs) effectively to both technical and non-technical stakeholders.
 * Build and maintain a strong network across the Data and Engineering organizations to ensure ML systems integrate seamlessly with the wider platform.