Computational Biologist Intern

About the Role

We are seeking a highly motivated, innovative, and rigorous Computational Biologist Intern to work with our team. You will push the limits of what can be inferred from our growing dataset of spatial biology data from cancer tissue samples, while collaborating with experimental biologists to design and develop further improvements to the spatial biology platform. Depending on your strengths and interests, projects may include:

• Analyzing spatial biology data to learn which therapies work and why.
• Building or improving pipelines that turn microscopy images into usable signal.
• Prototyping ML methods to rank candidate therapies, including proteins.
• Mining our growing dataset to answer specific biological questions.

You will see how AI-driven drug discovery works end to end, and you will sit directly between the people building the machine learning and the people running the biology. This internship can transition to a full-time opportunity.

Requirements & Tech Stack

• Solid programming in Python (numpy, pandas, scikit-learn, or pytorch a plus).
• Exposure to machine learning, data science, or quantitative analysis.
• Comfort with biological concepts; a biology degree is not required.
• Eagerness to learn quickly in a fast-moving environment.

Nice to Have

• Any experience with image analysis, deep learning, or spatial / single-cell data.
• A GitHub repo, notebook, or paper with relevance to our scientific areas.

About Waypoint Bio

Waypoint Bio is a venture-backed biotech startup innovating in spatial and computational biology and their application to advancing cancer therapies. Our leading focus is the development of next-generation cell therapies for solid tumors. Our proprietary platform overcomes a major obstacle — the tradeoff between fast but inaccurate in vitro and more accurate but slow and expensive in vivo approaches — that holds back traditional cell therapy preclinical development. Combining spatial pooled screening, in silico engineering, and machine learning, we obtain both depth and breadth when screening cell therapy candidates: speed without compromising on in vivo accuracy. Our team is highly collaborative, interdisciplinary, and committed to developing breakthrough therapies to benefit patients’ lives.