About the role
At Bunkerhill, we are laser focused on helping researchers bring their algorithms to the clinic. To that end, we have launched a new initiative: to build a foundation model for medical imaging!
Today, when a researcher is interested in building an algorithm, they typically need to start from scratch (or use models pretrained on ImageNet) and train on a large quantity of data. To make this process faster and to lower the barrier to entry, we're building a foundation model that researchers can fine tune to build clinically-useful downstream algorithms.
The goal of this initiative is to enable researchers within the consortium to build robust, generalizable algorithms using much less data and then use the Bunkerhill Consortium to validate their algorithm, obtain regulatory clearance, and distribute it for clinical use!
We are looking for a full-time Machine Learning Engineer to join our in-person team in our office in SoMa in SF.
Responsibilities include
- Design, develop, and deploy machine learning algorithms and models, with a focus on Deep Learning and Computer Vision techniques.
- Define and operate batch processing pipelines.
- Create and maintain user documentation to help researchers onboard their AI models.
- Develop large deep learning models with self-supervised learning on multimodal data.
- Communicate findings to other engineers, operators, and leadership.
- Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions.
- Conduct research to stay abreast of the latest advancements in machine learning, deep learning, and computer vision.
- Optimize algorithms for performance, scalability, and efficiency, considering real-world deployment constraints.
- Evaluate and validate models using appropriate metrics and datasets, ensuring robustness and reliability.
- Mentor junior team members and contribute to knowledge sharing within the organization.
Requirements
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.
- 3+ years of work professional work experience in a fast-paced, high-growth environment.