Staff Machine Learning Engineer (f/m/d)

We see the potential inside everything and everyone. Starting with you.

Orbem uses AI to industrialize MRI, delivering non-invasive insight into everything from fruits to eggs to the human body. We are transforming what is becoming the world’s largest biological dataset into actionable intelligence to reduce waste, increase quality, and accelerate the shift toward a more sustainable and healthier future.

Headquartered and founded in Munich, Germany, with an office now in Houston, Texas, our world-class team is scaling inside-out technology to transform how humanity sees and understands biological matter.

Help us see what’s possible. Join us.

Staff Machine Learning Engineer (f/m/d)

• Starting date: As soon as possible

• Yearly Salary: €90,000 - €100,000 (fixed range, annual gross)

• Stock Options: €40,000 to €80,000

• Benefits: Up to €5,000 annually

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

Your role

As a Staff Machine Learning Engineer, you will be at the forefront of building the intelligent systems that power Orbem’s next-generation technology. You’ll design and deliver scalable, production ready ML solutions that move seamlessly from prototype to real-world deployment. Partnering closely with data scientists and software engineers across teams, you’ll shape the core architecture that brings breakthrough ideas to life.

You’ll be a driving force for excellence, defining best practices, elevating engineering standards, and championing high-quality machine learning at every stage. With your production mindset, passion for impact, and vision for what’s possible, you’ll help unlock the next era of AI-powered MRI imaging propelling Orbem toward its ambition of becoming a true Machine Learning powerhouse.

Your day to day

On a typical day you would: 

• lead design and implementation of ML systems.

• build and improve code for training ML models, prioritizing modularity, scalability and robustness.

• collaborate with data scientists in training and fine-tuning ML models.

• optimize and troubleshoot production ML code and models, focusing on latency and memory footprint.

• deploy and monitor ML models in production, ensuring model stability.

• grow the know-how in production ML tools and concepts within the team.

• guide the team in adopting the best ML engineering practices, ensuring a consistent and production-ready approach across all AI developments.