Senior, AI Engineer Imaging

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come.

Join Roche, where every voice matters.

As an imaging-focused machine learning engineer within the AI Enablement team, you will be working closely with folks that span the gamut across computational scientists, biologists, bench scientists, AI/ML experts, product leaders, software engineers, and everyone in between. Your work will help bridge scientific intuition with quantitative analysis, enable richer phenotypic analysis, and provide new multimodal capabilities across large-scale, state-of-the-art datasets.

You'll build, own, and constantly improve scalable AI/ML and computer vision systems that unlock the potential of our diverse scientific imaging data (e.g., microscopy, digital pathology, biomedical imaging, etc), accelerating the discovery and development of life‑changing treatments for patients.

Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines tailored for high‑throughput scientific imaging data processing and visualization. Build, evaluate, and fine‑tune state‑of‑the‑art computer vision and deep learning models (e.g., cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration) applied to biological and clinical imaging modalities. Build tools to evaluate AI/ML and vision model performance, establish benchmark datasets, and quantify model quality, robustness, and drift over time.

Develop and maintain scalable cloud infrastructures and data pipelines for collecting, structuring, storing, and accessing large‑scale multi‑dimensional imaging datasets (e.g., OME‑TIFF, Zarr, HDF5). Contribute to architectural decisions, code reviews, and the evolution of engineering and MLOps processes for computer vision models. Stay up‑to‑date with emerging computer vision technologies and industry best practices while promoting a culture of continuous learning, collaboration, and curiosity.

PhD, Master’s with 3–5 years, or Bachelor’s with 4–7 years experience with a degree in Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or a related technical field with focus on machine learning engineering and computer vision. Strong proficiency with AI/ML frameworks and computer vision libraries (e.g., PyTorch, Tensor Flow, OpenCV, MONAI, Scikit‑Image, torch vision, Napari, or Bio‑Formats). Deep expertise in computer vision algorithms (e.g., CNNs, Vision Transformers, UNet, SAM/Segment Anything, Cellpose).

Expert knowledge of machine learning theory, image processing fundamentals, object detection, instance segmentation, and quantitative image analysis. Demonstrated experience working with high‑dimensional scientific imaging data (e.g., fluorescence/brightfield microscopy, digital pathology whole slide images, high‑content screening, multiplexed imaging, etc).

Experience with Kubernetes, relational databases, No

SQL databases, object storage, and cloud‑native architectures in public clouds (ideally AWS). Excellent communication skills and ability to build trusted partnerships with scientific collaborators, bioimage analysts, and cross‑functional teams. Experience with multi‑modal data fusion (combining scientific imaging with genomics, transcriptomics, or clinical tabular data) or in a…