Do you want to be part of a world wide organization recognized as World's Best Workplace with the mission of saving patients through advanced biotech technologies?
Our client's products reach over 10 million patients worldwide, that is why we are supporting them in looking for a Machine Learning engineer to support the organization’s broader Reliability Engineering initiatives through the application of machine learning and data-driven methods.
Contract length: 12 months (possibility of extension or permanent contract after)
Pay range (not negotiable): 4700 to 4950 a month/gross (not enough for Visa sponsorship)
Main Tasks
- Support the full machine learning model lifecycle including data preparation, model development, validation, deployment, and continuous improvement.
- Collect, preprocess, and analyze data from facility systems and manufacturing environments.
- Interpret system and machine behavior using vibration, frequency, force, position, temperature, and image data.
- Research and propose suitable machine learning and deep learning models for defined use cases.
- Develop, train, and validate models for condition-based monitoring, predictive maintenance, and anomaly detection.
- Build production-level Python pipelines for model training, validation, and deployment.
- Design and implement dashboards for monitoring data and model outputs using Grafana.
- Perform systematic model evaluation, validation, and monitoring for long-term reliability.
- Monitor deployed machine learning models to detect model drift and data drift.
- Collaborate with technical leads, automation engineers, vendors, and global data science teams.
- Contribute to advanced industrial AI initiatives such as Computer Vision for Assisted Line Clearance.
- Support development of data-driven models for Digital Twin systems used for process monitoring, simulation, and optimization.
Requirements
- MSc in Computer Science, Machine Learning, Mechatronics, or related fields with 3+ years of experience, or BSc with 5+ years of experience.
- Proficiency in Python, including libraries such as pandas, scikit-learn, PyTorch, and TensorFlow.
- Experience with Databricks.
- Knowledge of anomaly detection methods, probabilistic models, and practical ML model deployment.
- Ability to interpret physical machine behavior from sensor data, including pumps, compressors, and assembly systems.
- Ability to write clean, production-level code.
- Proficiency with Git for version control.
- Familiarity with DevOps tools and practices including Docker and CI/CD workflows.
- Ability to independently design and implement end-to-end ML solutions.
- Fluency in English, written and spoken.
Nice to have:
- Experience in predictive maintenance use cases (nice to have).
- Experience deploying ML in regulated environments such as GMP (nice to have).
- Experience building dashboards and visualizations in Grafana (nice to have).
- Knowledge of computer vision techniques including CNNs, Vision Transformers, PatchCore, and PaDiM (nice to have).
- Familiarity with AWS services such as SageMaker and S3 (nice to have).
