Research Scientist - Deep Learning for Biomedical Imaging
Position: Research Scientist - Deep Learning for Biomedical Imaging
Locations: Palo Alto, CA (on-site)
Department: Engineering
Employment Type: Full-Time, On-site
Role Overview
This role is for an outstanding researcher passionate about developing and applying advanced AI/ML techniques to the analysis and generation of imaging data. The successful candidate will drive innovation at the intersection of AI and biomedical imaging, contributing to projects that range from pathology and molecular imaging to clinical modalities like MRI and CT. You will work in a highly collaborative, interdisciplinary environment where your expertise will directly impact the next generation of diagnostic and therapeutic technologies.
Key Responsibilities
Advanced Model Development: Design, implement, and debug deep learning models for analyzing, modeling, and generating biomedical imaging data.
Research & Innovation: Lead research initiatives and contribute to the development of cutting-edge AI methods, with a focus on generative models, graph neural networks, and large-scale deep learning applications.
Data-Driven Insights: Apply advanced ML/AI techniques to extract features, segment images, and perform both supervised and unsupervised analyses on diverse imaging modalities (e.g., pathology images, microscopy, MRI, CT).
Scalable Solutions: Develop and optimize end-to-end AI pipelines, ensuring scalability and efficiency through distributed training and inference on accelerators.
Interdisciplinary Collaboration: Work closely with a multidisciplinary team to integrate AI models into broader biomedical applications and communicate research insights across a variety of technical and non-technical audiences.
Continuous Learning: Stay current with state-of-the-art research in both AI/ML and biomedical imaging, contributing to publications and open-source projects where applicable.
Essential Qualifications
Academic Background: PhD (or evidence of equivalent expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
Research Record: Proven track record in research and innovation, with contributions to top-tier AI/ML and/or core biology conferences and journals (e.g., NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, Nature, Science, Cell).
Technical Proficiency: Extensive experience in developing deep learning models using popular frameworks such as JAX, TensorFlow, or PyTorch.
Solid Foundations: Strong theoretical background in statistics, optimization, graph algorithms, and linear algebra, with hands-on experience building models from the ground up.
Interdisciplinary Passion: A demonstrated passion for research at the intersection of AI and Biology, and the ability to rapidly acquire domain-specific knowledge.
Engineering Practices: Familiarity with software engineering best practices, including version control, documentation, and open-source contributions.
Preferred Qualifications
Industry or Postdoc Experience: 3+ years of post-PhD experience in an industry or postdoctoral role.
Biomedical Imaging Expertise: Hands-on experience with biomedical imaging modalities such as pathology (H&E), molecular/microscopy imaging, MRI, CT, etc., including tasks like segmentation and feature extraction.
Scalable ML Solutions: Experience with large-scale distributed training and inference.
Deep Domain Knowledge: Advanced understanding of various model architectures and methodologies specific to imaging data analysis.
Compensation: $100,000 - $300,000 per year, commensurate with experience.
Join the Revolution
If you are eager to contribute to transformative advances in biomedical imaging and be part of a high-energy startup environment that is shaping the future of healthcare, we invite you to apply and help redefine what's possible through the power of generative AI.