Head of Algorithms- Chronic Disease Management

About Us

How many companies can say they’ve been in business for over 179 years?! Here at ZEISS, we certainly can! As the pioneers of science, ZEISS handles ever‑changing environments in a fast‑paced world, meeting it with cutting‑edge technologies and continuous advancements. ZEISS believes that innovation and technology are the key to a sustainable future and solutions for global change. We have a diverse range of portfolios throughout the ZEISS family in segments like Industrial Quality & Research, Medical Technology, Consumer Markets and Semiconductor Manufacturing Technology.

We are a global company with over 46,000 employees and have over 4,000 in the US and Canada alone! Make a difference, come join the team!

Location/Region:
This position is located in Dublin, CA

This role will drive the development and deployment of advanced algorithms for ophthalmic imaging and chronic disease management, including AI‑powered diagnostics, predictive analytics, and multimodal data fusion. You will lead a team of algorithm engineers and data scientists, collaborate with cross‑functional stakeholders, and shape the future of ZEISS’s AI strategy in ophthalmology. This position reports directly to the Head of R&D – Chronic Disease Management and plays a critical role in defining the technical roadmap and innovation strategy for the division.

You will also work closely with the Advanced Development of the Strategic Business Unit Ophthalmology (OPT) and the Digital Business Unit of Carl Zeiss Meditec (DBU) to align long‑term research initiatives with product development and strategic innovation goals.

• Lead the design and implementation of algorithms across key areas:
  • Image segmentation (e.g., retinal layers, optic disc, macula)
  • Disease detection and classification (e.g., glaucoma, AMD, diabetic retinopathy)
  • Progression prediction models for chronic conditions
  • Multimodal fusion algorithms integrating OCT, fundus, and angiography data
  • Oculomics‑based systemic disease prediction (e.g., cardiovascular, neurodegenerative)

• Define and execute the AI strategy for chronic disease management within ophthalmology.
• Apply advanced machine learning techniques:
  • Deep learning (CNNs, transformers)
  • Self‑supervised and semi‑supervised learning
  • Federated learning for privacy‑preserving model training
  • Explainable AI (XAI) for clinical interpretability
• Ensure responsible AI practices, including bias mitigation, transparency, and clinical safety.
• Utilize frameworks such as Tensor Flow, PyTorch, ONNX, and OpenCV.
• Collaborate on AI deployment pipelines, model optimization, and real‑time inference on embedded platforms.

• Build, mentor, and lead a high‑performing team of algorithm developers and data scientists.
• Foster a culture of innovation, technical excellence, and accountability.
• Drive hiring, performance management, and career development.

• Work closely with software and hardware engineering, clinical affairs, regulatory, product management, and quality teams to ensure seamless integration of algorithms into ZEISS platforms.
• Collaborate with the Advanced Development OPT and the DBU to align on foundational research, technology scouting, and long‑term innovation.
• Partner with academic institutions and key opinion leaders to co‑develop and validate novel approaches.

• Develop and maintain comprehensive documentation for algorithm traceability, reproducibility, and regulatory compliance (FDA, CE).
• Support clinical trials and validation studies for algorithm performance.

• Ph.D. or Master’s in Computer Science, Electrical Engineering, Biomedical Engineering, or related field.
• Minimum 5 years of hands‑on experience in algorithm development, preferably in medical imaging or ophthalmology.
• Minimum 3 years of experience in leading algorithm or data science teams.
• Strong programming skills in Python, C++, or Java.
• Proven experience with AI/ML frameworks and deployment in clinical or regulated environments.
• Deep understanding of ophthalmic imaging modalities and…