Bunkerhill Health is at the forefront of revolutionizing medical imaging through artificial intelligence. They are embarking on an ambitious initiative to build a foundation model for medical imaging that will transform how researchers develop and deploy clinical algorithms. This role offers a unique opportunity to join their in-person team in San Francisco's SoMa district.
The position involves working on cutting-edge deep learning and computer vision technologies specifically tailored for medical applications. As a Machine Learning Engineer, you'll be instrumental in developing foundation models that researchers can fine-tune to build clinically-useful downstream algorithms with less data than traditionally required. This work directly impacts the healthcare field by lowering barriers to entry for medical imaging algorithm development.
The role combines technical expertise in machine learning with practical healthcare applications. You'll be working with state-of-the-art deep learning frameworks and handling multimodal data while collaborating with researchers and healthcare professionals. The position offers competitive compensation at $156,000 and comes with comprehensive benefits including health coverage and equity.
This is an ideal opportunity for someone with a strong technical background who wants to make a meaningful impact in healthcare. You'll be part of a dynamic team focused on innovation and collaboration, with opportunities to mentor others and contribute to groundbreaking developments in medical imaging AI. The position requires a master's degree and expertise in Python and major ML frameworks, making it perfect for someone who combines academic knowledge with practical engineering skills.
Working at Bunkerhill means being part of a mission to transform how medical imaging algorithms are developed and deployed in clinical settings. You'll be involved in the entire pipeline from research to deployment, ensuring that your work directly contributes to improving healthcare delivery through advanced AI technologies.