Machine Learning Engineer

You will be joining a global leader in the biotechnology and pharmaceutical industry, focused on developing innovative therapies for serious diseases. The organization operates at the forefront of advanced manufacturing, where digitalization, data, and automation play a key role in ensuring high-quality and efficient production processes.

Within this environment, there is a strong focus on Smart Digital Manufacturing, leveraging data and machine learning to improve equipment reliability, optimize performance, and enable predictive maintenance strategies.

About the role
As a Machine Learning Engineer, you will be part of the Equipment Engineering team, contributing to the development of data-driven solutions that enhance manufacturing reliability and efficiency.
You will work with complex datasets from facility and manufacturing systems (such as cooling systems, compressors, pumps, and production lines) to generate actionable insights into equipment health and performance. By developing and deploying machine learning models in production environments, you will play a key role in enabling predictive maintenance and reducing downtime.
As the program evolves, you may also contribute to advanced industrial AI applications, including computer vision solutions (e.g. assisted line clearance) and Digital Twin models for simulation, monitoring, and optimization of manufacturing processes.
Key responsibilities

• Manage the full machine learning lifecycle: data collection, preprocessing, model development, validation, deployment, and continuous improvement
• Analyze data from industrial systems such as compressors, pumps, and manufacturing equipment
• Interpret complex sensor data (e.g. vibration, frequency, temperature, force/position, and image data)
• Research and implement suitable machine learning and deep learning models
• Develop models for predictive maintenance, anomaly detection, and condition-based monitoring
• Build scalable, production-level Python pipelines for model training and deployment
• Design and implement dashboards (e.g. Grafana) to monitor data streams and model performance
• Perform model validation and monitoring to ensure robustness and long-term reliability
• Detect and respond to data drift and model performance issues
• Collaborate with engineers, technical leads, vendors, and global data science teams

Your Profile:
Education & experience

• MSc in Computer Science, Machine Learning, Mechatronics, or a related field + 3+ years of relevant experience
  or
• BSc in a similar field + 5+ years of relevant experience

Technical skills

• Strong Python skills (pandas, scikit-learn, PyTorch and/or TensorFlow)
• Experience with Databricks
• Solid understanding of machine learning and deep learning (e.g. anomaly detection, probabilistic models)
• Experience deploying models into production environments

Systems knowledge

• Ability to interpret physical machine behavior using sensor data

Software engineering & DevOps

• Experience writing clean, scalable, production-level code
• Proficiency with Git
• Familiarity with Docker, CI/CD, and DevOps practices

Other

• Ability to independently design and deliver end-to-end solutions
• Fluent in English (written and spoken)

Nice to have

• Experience with predictive maintenance or industrial data use cases
• Experience in regulated environments (e.g. GMP)
• Experience with Grafana dashboards
• Knowledge of computer vision (CNNs, Vision Transformers, PatchCore, PaDiM)
• Familiarity with AWS (SageMaker, S3)