AI Engineer - Vision Foundation Model Pretraining - Howard Hughes Medical Institute - United States

Primary Work Address: 19700 Helix Drive, Ashburn, VA, 20147 Current HHMI Employees, click here to apply via your Workday account. TLDR: Build the model backbone for the next era of AI-powered spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position. Describe a deep learning project you have executed—ideally a creative use of a vision transformer, U-Net architecture, or Diffusion model that you trained yourself. Projects in computer vision for microscopy image analysis are especially relevant. Include a link to a code repository if possible. If you contributed to a joint project, please describe your specific contributions. Briefly discuss the project's results, limitations, and challenges you encountered. Finally, include a link to your GitHub profile, personal website, or similar and/ or any relevant projects at the bottom of your cover letter. About the role: AI@HHMI: HHMI is investing $500 million over the next 10 years to support AI-driven projects and to embed AI systems throughout every stage of the scientific process in labs across HHMI. The Foundational Microscopy Image Analysis (MIA) project sits at the heart of AI@HHMI. Our ambition is big: to create one of the world’s most comprehensive, multimodal 3D/4D microscopy datasets and use it to power a vision foundation model capable of accelerating discovery across the life sciences. We are seeking a highly skilled AI Research Engineer to join our team and advance our AI-driven scientific initiatives. You will develop and deploy a self-supervised pre-training pipeline for learning from a large-scale microscopy dataset. You will work with expert computational scientists, data engineers, and experimentalists to train models that learn foundational embeddings that can be used across a wide range of microscopy modalities and applications. In collaboration with other engineers and scientists, you will use these models for scalable vision task