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Network Bio

AI models trained on biobank data

AI Scientist

AI ResearchConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • First-author contribution to state-of-the-art AI model publications in competitive journals or conferences.
  • Proven ability to drive end-to-end development on major projects.
  • Ph.D. in Machine Learning, Bioinformatics, or a related field.
  • Open to recent Ph.D. graduates, postdocs, and early-career candidates with up to 5 years of post-Ph.D. experience.

Responsibilities

  • Design, optimize, and validate deep-learning models for high-dimensional biological data, including histopathological slides, spatial transcriptomics, and structured clinical datasets.
  • Translate biological insights into modeling strategies.
  • Support model interpretability, robustness, and validation in regulated or translational research contexts.
  • Architect and build agentic AI systems that reason over multiple steps, plan autonomously, and orchestrate external tools.
  • Develop LLM-driven agents that retrieve, prioritize, and synthesize evidence with memory and feedback loops that let them revise hypotheses and adapt as new data emerges.
  • Engineer production-grade infrastructure that unifies structured and unstructured biomedical data at scale, and rigorously assess the robustness, reliability, and interpretability of autonomous systems operating in high-stakes clinical contexts.
  • Apply software engineering best practices to ensure reproducible and efficient scientific discovery.
  • Develop tools and services that support experiment tracking, reproducibility, and quality control.
  • Perform software validation and documentation in research or regulated diagnostic contexts.
  • Rapidly broaden knowledge of biological concepts and technical skills outside one's primary expertise.
  • Leverage AI coding agents for implementation and testing, focusing on architectural rigor and system logic.
  • Audit AI-generated code and scientific reasoning to identify subtle hallucinations before they reach company systems.

About the Company

Network Bio

AI models trained on biobank data

Network Bio creates disease-specific AI models from large-scale human biological data to advance personalized medicine. It links tissue samples, blood, molecular data, and long-term clinical outcomes from major U.S. biobanks, harmonizes them into AI-ready datasets, and trains a bio-native AI system that can apply learned biology principles across diseases to support diagnostics, biomarker discovery, patient stratification, and drug development. Unlike many peers, it builds a shared, standardized data network across multiple institutions for broader model generalization and cross-disease use, backed by partnerships such as NVIDIA and corporate validation deals. Its goal is to develop General Medical Intelligence that understands disease biology at a fundamental level and applies it across medical domains.

Company size
11-50
Company stage
Early VC
Total funding
$50M
Headquarters
New York City, United States
Founded
2022

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