(Senior) Machine Learning Engineer (all)

As a Senior Machine Learning Engineer, you'll be a key contributor to our AI team, developing deep learning computer vision models for medical applications. Your focus will be on designing, training, and validating ML solutions that analyze radiological imaging data (MRI, CT) to support accurate and efficient diagnoses. You'll work hands-on with modern deep learning frameworks and take responsibility in mission-critical ML design choices to ensure that models meet clinical and regulatory standards. In this role, you'll have the chance to drive the end-to-end evolution of medical AI-transforming complex research experiments into high-impact, production-ready diagnostic tools that define the future of radiological care., * Advance the development of high-performance computer vision architectures to detect and diagnose medical conditions within complex radiological datasets (MRI, CT).

• Design and implement rigorous validation frameworks to ensure model robustness, clinical efficacy, and compliance with medical device certification standards.
• Engineer solutions with clinical trust at the core, prioritizing model interpretability and uncertainty quantification to provide actionable insights for healthcare professionals.
• Optimize scalable ML pipelines within a modern Docker and AWS ecosystem, ensuring a seamless transition from experimental research to production-grade deployment.

Do you have experience in Unit testing?, Do you have a Master's degree?, * Ph.D. or Master's in Computer Science, Machine Learning, Engineering, Mathematics, or a related quantitative field.

• 3+ years of industry experience specifically focused on computer vision for medical applications.
• Expertise in Python and PyTorch, with a deep understanding of medical image processing techniques and data formats.
• Strong theoretical and practical understanding of modern computer vision architectures for 3D image segmentation and object detection (e.g. CNNs, ViT).
• Familiarity with at least two of the following areas:

• Self-supervised or semi-supervised learning
• Computer vision foundation models
• Anomaly detection
• Active learning

• Proven ability to contribute to large-scale codebases and handle complex datasets in a collaborative environment.
• Commitment to robust software engineering practices: including version control (Git), unit testing, CI/CD, and writing clean, maintainable code.

• Focus on what matters: At our company, competence, personality, and commitment are more important than formalities or the study program you chose.
• Your Impact: Your work helps make disease diagnosis faster, more precise, and more manageable.
• Your Team: Join a highly motivated, interdisciplinary team with fast decision-making processes and a collaborative atmosphere.
• Freedom to choose: Work remotely or from our office in Berlin - Kreuzberg - whatever suits you best.
• Benefits: Use your personal benefits budget flexibly and choose the perks that suit you best!