AI Research Intern (PhD), AI Research, Science

Biohub•Redwood City, CA

4h•$64 - $88•Hybrid

About The Position

Biohub is leading the new era of AI-powered biology to cure or prevent disease through its 501c3 medical research organization, with the support of the Chan Zuckerberg Initiative. The Team Biohub is building the first large-scale initiative combining frontier artificial intelligence and frontier biology to cure or prevent all disease. In 2025, Biohub welcomed the EvolutionaryScale team - recognized for creating large-scale frontier AI systems for the life sciences - to accelerate the mission. Led by co-founder Alex Rives, who now serves as Head of Science at Biohub, the integrated organization is scaling up the compute and research infrastructure needed to build next-generation models of biology-powered by the largest cluster dedicated to open science. Backed by the Chan Zuckerberg Initiative (CZI), Biohub operates as a nonprofit organization uniting leading scientists, engineers, and technologists across hubs based in the San Francisco Bay Area, New York, and Chicago. This is an opportunity to shape the future of biological research by pushing the boundaries of what AI can achieve in science. You'll work alongside leading experts in AI and biology, with the resources and mandate to tackle some of the most important questions in human health. You will be part of the AI Research team, which focuses on training biological world models and reasoning systems at the frontier of data, scale and compute. We are organized as a mostly flat organization of researchers to enable rapid and dynamic AI development. The Opportunity From day one, you'll be embedded in one of our flagship AI research efforts — working with experienced researchers and a strong engineering team on the next generation of biological foundation models, models with deep biological reasoning capabilities, and AI agents that can formulate hypotheses, design experiments, and learn from experimental biological outcomes. You will be paired with a dedicated mentor and given scientific ownership over your contributions, backed by world-class GPU infrastructure to test and scale your ideas. Your work will feed into our ambitious research projects that push the frontier of what is possible. We share our progress through leading journals, alongside open releases of data, models, and tools used by millions of researchers around the world. Internships are structured for 12 weeks or longer, with a preference for extended commitments; however, we are flexible with arrangements for exceptional candidates.

Requirements

Active enrollment in a PhD program in Machine Learning/AI, computational biology, physics, applied math, or a related quantitative field.
A track record of creative, high-quality research demonstrated through publications, fellowships, grants, patents, or equivalent achievements.
Experience implementing deep learning models with PyTorch or equivalent frameworks.
Ability to work independently and collaboratively on open-ended research problems.
Strong communication skills across disciplines.
Authorization to work in the US (Biohub is unable to sponsor current or future work authorization for this position)

Nice To Haves

Experience with large-scale model training
Familiarity with biological data modalities (sequences, structures, imaging, omics)
Experience with dataset curation, evaluation design, or ML infrastructure at scale

Responsibilities

Conduct research on training deep learning models in biology at the frontier of data, scale, and compute.
Contribute to key aspects of our frontier AI systems for biology — including data pipelines, model training, evaluations, and applications to specific biological research directions.
Implement and iterate on research ideas rapidly, writing clean, production-quality code alongside other researchers and engineers.
Collaborate with cross-functional partners including wet-lab scientists, communicating research ideas, progress, and results across disciplines.
Contribute to team publications and open-source releases that advance the field.
Depending on your project, you may focus on one or more of the following:
Training biological world models across modalities and scales.
Training and fine-tuning LLMs for biological reasoning and understanding.
Designing and building agentic systems capable of conducting autonomous biological investigations.