About Biohub
Biohub is a pioneering initiative that brings together frontier AI models, massive compute power, and advanced experimental capabilities to accelerate scientific discovery. Our mission is to build a general-purpose system that integrates frontier AI, biological foundation models, and lab capabilities with the ultimate goal of curing disease. We operate one of the largest AI compute clusters dedicated to biology, working across three frontier research institutes with leading biologists. We are committed to building frontier AI for fundamental science as open science, at an unprecedented scale, addressing some of the most challenging and impactful problems.
Our Research Areas
- Frontier molecular modeling: Including protein language models (e.g., ESM) and structure prediction (e.g., ESMFold).
- Scaled biological foundation models: Trained on some of the largest GPU clusters dedicated to science.
- Imaging foundation models: Trained across the world's largest microscopy datasets.
- Reasoning and agentic systems: Connecting frontier LLMs with biological foundation models.
- Mechanistic interpretability: Extracting new biological knowledge directly from model weights.
- Scientific data at unprecedented scale: Developing AI systems to collect, curate, and learn from rich biological datasets.
What You'll Do
- Build on and advance AI systems at the frontier of biology to accelerate scientific discovery.
- Design novel model architectures and scale pre-training pipelines for biological foundation models.
- Develop post-training systems for biological discovery, including RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks.
- Design evaluation frameworks and AI systems in collaboration with scientists, grounded in real biological outcomes and impact.
- Engage with the wider scientific community through publications, open-source releases, and collaborations.
What You'll Bring
- PhD in computer science or a related field, or equivalent experience.
- Significant experience building and scaling deep learning systems, ideally in research-driven environments.
- Strong engineering and scientific fundamentals.
- Comfort with ambiguity, rapid iteration, and loosely-defined research problems.
- Strong communication skills across technical and scientific audiences.