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Biohub Tech Stack

AI infrastructure and biology foundation models for disease research

Biotechnology Research Redwood City, California 201–500 employees Nonprofit
biohub.org LinkedIn

Biohub operates a research-heavy nonprofit combining petabyte-scale compute infrastructure with applied AI for biological discovery. The tech stack reveals a production ML operation: PyTorch, TensorFlow, Hugging Face, Ray, Kubernetes, GPU, and Databricks sit alongside specialized biology tools (10x Genomics, FIB-SEM, Opentrons). Active hiring skews toward senior research and engineering roles, with ongoing work on biology foundation models, multi-agent systems for scientific tasks, and spatiotemporal multi-omics platforms—indicating they've moved past proof-of-concept into systems that integrate AI inference with wet-lab automation at scale.

Tech Stack 29 technologies

Core StackPython MATLAB C++ Kubernetes Delta Lake Databricks Apache Iceberg AWS SolidWorks FIB-SEM 10x Genomics LIMS Slurm InfiniBand GPU ESMFold Argo Workflows Ray AWS ParallelCluster Vast Data Creo Fusion 360 Opentrons Coreweave GCP
AdoptingPyTorch TensorFlow Hugging Face RAPIDS

What Biohub Is Building

◆Challenges

  • Scaling ai infrastructure for biology
  • Integrating ai with lab capabilities
  • Accelerating scientific discovery
  • Managing petabyte-scale data i/o
  • Running model inference at scale
  • Pipeline reliability and observability
  • Integrating complex biological protocols
  • Moving beyond commoditized automation
  • Reliable high-performance compute infrastructure
  • Debugging distributed systems failures

▲Active Projects

  • Multi-agent systems for long-horizon scientific tasks
  • Immune cell reprogramming for age-related diseases
  • Spatiotemporal multi-omics platform
  • Reinforcement learning for biological discovery
  • Pre-training infrastructure reliability
  • Biology foundation models
  • Biology foundation model development
  • Model deployment and artifact tracking
  • Gpu-native data loading pipelines
  • Advanced light-sheet microscope development for zebrafish imaging

Hiring Activity

Accelerating35 roles · 30 in 30d

Department

Research
21
Engineering
11
Data
5

Seniority

Senior
14
Mid
12
Staff
6
Director
2
Junior
2
Principal
1

Notable leadership hires: Immune Cell Reprogramming Lead

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About Biohub

Biohub is a nonprofit research organization in Redwood City focused on advancing AI-powered biology to accelerate disease understanding and treatment. The organization combines computational infrastructure with experimental capabilities: they develop biology foundation models, multi-omics platforms, and automated protocols for cell reprogramming and infectious disease research. Operations span 201–500 employees across research, engineering, and data teams, with hiring active in the United States and Canada. Core challenges center on managing infrastructure complexity—petabyte-scale data pipelines, GPU-native data loading, distributed compute reliability—while integrating AI models with laboratory automation.

HeadquartersRedwood City, California
Company Size201–500 employees
Hiring MarketsUnited States, Canada

Frequently Asked Questions

What is Biohub's tech stack?

Biohub uses Python, C++, MATLAB, PyTorch, TensorFlow, Kubernetes, Ray, Databricks, Delta Lake, 10x Genomics, FIB-SEM microscopy, Opentrons automation, AWS, and specialized HPC infrastructure (Slurm, InfiniBand, GPU).

What is Biohub working on?

Projects include biology foundation models, multi-agent systems for scientific discovery, spatiotemporal multi-omics platforms, immune cell reprogramming for age-related diseases, reinforcement learning for biological discovery, and advanced light-sheet microscopy for zebrafish imaging.

How this profile is built

Biohub's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.