AI ENGINEER (F/M/D)

Founded in 1921, OLDENDORFF CARRIERS combines its history as a German shipowner with the network of one of the world’s leading drybulk operators. We currently control some 700 chartered and owned vessels of 40 mio tdw, and we carry around 380 mio tons of raw materials and semi-finished products across the seven seas each year. Our customers can expect 100% performance. All the way.
Over the past four years, Oldendorff has transformed its IT landscape, bringing it up to cutting-edge standards and laying the foundation for future innovations. In October 2024, we launched our Data & Analytics Department, alongside our Machine Learning division, the Data & Analytics Lab (D&A Lab).
As data and machine learning become central to our strategy, we are seeking a talented AI Engineer who is eager to contribute to shape the future of Oldendorff by turning bold ideas into tangible results. In this role, you will work at the intersection of machine learning, software engineering and business innovation - building innovative tools that drive smarter decisions, streamline operations and create real impact across our global network. You will collaborate closely with our Business and Data & Analytics teams to unlock the full potential of our data, strengthen our market leadership and contribute to our goal of carbon neutrality by 2050.

KEY QUALIFICATIONS

• Master’s in Computer Science, Mathematics, Physics or a related discipline (or equivalent practical experience). A PhD is a plus.
• Solid theoretical and applied knowledge in machine learning, statistics and related fields.
• Strong proficiency in Python and Git, with sound software engineering practices.
• Basic proficiency of standard Python libraries such as Numpy, Pandas, Scikit-learn. PyMC is a strong plus.
• Experience with deep learning libraries such as PyTorch and/or JAX.
• Ability to work independently and collaboratively, with strong problem-solving and communication skills.

PREFERRED / VALUED QUALIFICATIONS

• Experience in developing end-to-end machine learning pipelines.
• Research contributions in areas such as time series forecasting, Bayesian modelling or optimization.
• Familiarity with MLOps practices, data platforms (e.g., Databricks), cloud infrastructure (Azure, AWS, GCP) and containerization (Docker).
• Experience working with large-scale or industrial datasets.
• Understanding of evaluation, validation, and deployment challenges of ML systems.

• Design, implement and maintain ML pipelines across supervised, unsupervised and reinforcement learning paradigms.
• Conduct research and experimentation on topics such as time series forecasting, optimization and decision-support systems.
• Contribute new ideas, approaches and methodologies to solve complex business challenges.
• Collaborate with cross-functional teams to integrate ML solutions into production environments.
• Share knowledge and support colleagues - mentoring junior team members or learning from more experienced peers.
• Where relevant, publish results and findings in conferences and journals.