Research Engineer, AI

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. Our research spans: Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. 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 that connect frontier LLMs with biological foundation models Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled Join Our Team! As a Research Engineer, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale. What You'll Do Build on and advance the AI systems at the frontier of biology to accelerate science Design novel model architectures and scale pre-training pipelines for our biology foundation models. Build post-training systems that drive biological discovery: RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks. Design evaluation frameworks and AI systems with scientists across Biohub and beyond, grounded in real biological outcomes and real world impact. Engage the wider scientific community through impactful publications, open-source releases, and collaborations worldwide. What You'll Bring 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 Compensation The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $214,000 to $375,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. Benefits for the Whole You We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. Provides a generous employer match on employee 401(k) contributions to support planning for the future. Paid time off to volunteer at an organization of your choice. Funding for select family-forming benefits. Relocation support for employees who need assistance moving Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! #LI-Hybrid