Machine Learning Engineer (f/m/d)

Orbem is an impact-driven deep-tech scaleup with global reach, founded in Munich, Germany and now expanding internationally with our newest office in Houston, Texas. We develop fast, accurate, and accessible imaging solutions that provide access to otherwise unattainable sources of knowledge.

We seek to make a difference – and develop solutions to sustainably feed the world, accelerate the transition to a green economy, and transform disease detection.

Join us on our mission to unleash AI-powered imaging for everything and everyone.

Machine Learning Engineer (f/m/d)

• Starting date: As soon as possible

• Yearly Salary: €60,000 - €65,000 (fixed range, annual gross)

• Equity: €10,000 - €30,000 in company shares

• Benefits: Up to €5,000 annually

• Work model: Full-time, Hybrid (Munich)

Your role

As a Machine Learning Engineer at Orbem, you will turn cutting-edge research into production-ready solutions that power our AI-driven MRI technology. You’ll work closely with data scientists to transform models into scalable systems, and with software engineers to integrate them seamlessly into production.

Beyond building models, you’ll design robust, automated pipelines that accelerate experimentation and deployment. With a focus on performance, reliability, and maintainability, you’ll optimize inference, monitor systems, and champion best practices. Your work will enable a fast, high-quality AI lifecycle and unlock Orbem’s mission to scale impactful, AI-powered imaging globally.

Your day-to-day

In your daily activities, you will:

• Build and improve code for training machine learning models, prioritizing modularity, scalability and robustness.

• Automate pipelines for data ingestion and model training

• Optimize and troubleshoot production machine learning code and models, focusing on latency and memory footprint.

• Deploy and monitor machine learning models in production, ensuring model stability and reliable inference.

• Collaborate with data scientists in training and fine-tuning machine learning models.

• Adopt new tools and best practices in the area of machine learning and data processing.