Applied Machine Learning Engineer - IoT

Your role

Do you want to apply machine learning and signal processing to real industrial equipment operating worldwide? Join our Applied Machine Intelligence & IoT team and help transform data from more than 250,000 connected compressors into smart-maintenance and optimization solutions that create measurable customer value.

Is your ideal day one where you combine signal processing, machine learning, and a genuine curiosity about mechanical systems to build models that make real machines run better? Then you might be the perfect fit for our Applied Machine Intelligence & IoT team.
As an Applied Machine Learning Engineer in the Applied Machine Intelligence team (part of the Aftermarket R&D organization), you will turn sensor and machine data from our connected products into optimization and smart-maintenance models that deliver measurable value to our customers. You'll work across our global connectivity ecosystem and help bring proven optimization and uptime algorithms into our SMARTLINK AI platform.

As an Applied Machine Learning Engineer, you will analyze vibration, temperature, pressure, and operational data using both time-domain and frequency-domain techniques. By leveraging machine learning methodologies such as anomaly detection, predictive maintenance, classification, clustering, and forecasting, you will extract valuable insights from equipment data and translate them into actionable improvements.

You will be responsible for developing and enhancing models focused on energy efficiency, performance optimization, and smart maintenance for our connected product portfolio. In addition, you will integrate, validate, and deploy optimization algorithms within the Smart Events engine and the broader connectivity platform, ensuring that insights are effectively translated into customer value.

Working across the entire connectivity ecosystem, you will take an end-to-end approach, spanning from physical equipment and controllers to the generation of insights and the delivery of tangible business value to customers. You will also collaborate closely with colleagues from engineering, marketing, and operations to shape product roadmaps and deliver comprehensive solutions that address customer and business needs.

To succeed, you will need

At Atlas Copco Group, our approach for many positions is “hire for attitude and train for skill.” We support people through learning, coaching and on-the-job training. Do you only partly meet the profile below? No problem, we still encourage you to apply.

• You hold a master's degree in engineering (Electronics, Computer Science, or equivalent)
• You're skilled in Python, PySpark, Databricks, Pandas, Scikit-learn, MLflow
• You have hands-on experience with, or a strong interest in, signal processing and machine learning applied to sensor data
• You have a strong interest in mechanical systems (compressors, dryers, chillers or similar) and are eager to dive deeper into the domain
• You enjoy working in diverse, international teams and are comfortable collaborating across functions
• You are a problem-solver with a passion for innovation and a drive to continuously improve

In return, we offer

At Atlas Copco Group you will join an international and diverse environment with specialists from different disciplines. Our culture is rooted in Swedish values, which includes a family feeling, respect and diversity. We invest in our employees' development and well-being, and we provide a flexible work-life balance. With our inclusive and caring environment, you get the support and inspiration you need to grow. Here, your ideas are embraced, and you never stop learning.

You will have a comprehensive onboarding program, including guidance by a personal godmother/godfather. You will also have access to global job opportunities, as part of the Atlas Copco Group. We offer an interesting compensation and benefits package, including health insurance, paid leave and retirement benefits.

Job location

This role offers a hybrid working arrangement, allowing you to split your time between working remotely and being on-site at Airpower in Antwerp, Belgium (BE).