Machine Learning Engineer (all genders)

You turn models and data-driven methods into reliable services. The work concerns not only model quality but also reproducible processing, stable inference and measurable changes after a release. Together with software and data teams, you assess which models and operating approaches fit the task.

A model can perform well in an experiment and still fail in the application. You check data distribution, preprocessing and the separation of training and test data. Models, configuration and datasets are versioned transparently. At release, you compare quality, resource requirements and response time. Monitoring should detect changes in input data or declining output quality; a rollback to a previous version is tested explicitly.

• Implement reproducible training, validation and inference pipelines and make datasets and model versions traceable.
• Deploy models as services; evaluate resource requirements, throughput and response times through load tests.
• Define suitable quality metrics and comparison baselines, identify data leakage and document results clearly.
• Design monitoring for model quality and data changes and enable controlled updates and rollbacks.
• Link data and model versions with test metrics, configuration and release artefacts.
• Test inference under varying load and model rollbacks, including input formats.

What matters is demonstrable experience that is relevant to the role. It may come from appropriate vocational training, a degree, professional practice or a well-founded career change. We will align the specific level of responsibility with your knowledge and experience.

• Practical Python experience and strong foundations in statistics, model evaluation and data preparation.
• Experience with at least one ML framework and deploying models as applications or services.
• Understanding of automated tests, version control and clean separation of training and evaluation.

This knowledge is helpful, but you do not need to have all of it at once. In your application, describe where you have already gained practical experience and which areas you would like to explore in greater depth.

• Experience with MLOps, experiment tracking or feature pipelines.
• Knowledge of GPU inference, quantisation or reproducible container environments.

You deliver reproducible model services with documented limitations and transparent quality development. A better offline score only becomes a release once operational readiness and business value have also been assessed.

This role can be based in Cologne or performed remotely. We will agree the specific form of collaboration, project requirements and any necessary customer appointments before you start.

This role is offered as permanent employment on a full-time or part-time basis. We will agree the responsibilities, working hours, start date and compensation to suit the role and your level of experience.

Show us in your application which experience or interests you bring to this role. A CV and relevant project examples, certificates or work samples will help us assess your professional background. Please do not send confidential customer data, credentials or protected source code.

Tell us your preferred working model, your possible start date and whether you would like to work full-time or part-time.

If you have questions about the role, contact our recruitment team at hr@otoko.com. Inclusion in the applicant pool is voluntary and takes place only with separate consent; it is not a prerequisite for reviewing your application.