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Output Biosciences

AI-driven preventative therapeutics for chronic disease

Member of Technical Staff - Biological Data

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A PhD in computational biology, biophysics, structural biology, chemistry, biochemistry, or a related biological field with 2+ years of post-doctoral or industry research experience, or equivalent depth through a combined biology and computational background.
  • Deep understanding of molecular interactions, protein structure, and biological data at the molecular level, grounded in first principles.
  • Experience working with large-scale biological or molecular datasets, including sourcing, cleaning, integrating, and analyzing heterogeneous data.
  • Strong programming skills in Python and comfort building computational pipelines for data processing at scale.
  • Understanding of what machine learning models require from training data, including coverage, quality, balance, and evaluation rigor.
  • Approach data construction as a research problem, considering what data means, what signal it carries, and what is absent.

Responsibilities

  • Construct training datasets that capture how proteins and molecules interact, drawing from diverse biological data sources and extending them with understanding of molecular principles.
  • Develop methods to expand training data beyond public databases, using biological and chemical reasoning to create new training signal where current data is sparse or absent.
  • Design benchmarks grounded in real molecular phenomena to measure whether models have learned biologically meaningful capabilities rather than statistical shortcuts.
  • Develop data strategies in collaboration with model researchers, determining what the model should learn from, what biological signal to prioritize, and how to sequence learning across modalities.
  • Design approaches for integrating data across biological scales and modalities, building coherent training data from heterogeneous experimental and computational sources.
  • Design rigorous splitting and evaluation strategies that prevent leakage and ensure model capabilities generalize to real biological problems.
  • Stay current with biological data sources, experimental methods, and molecular databases, continuously identifying new sources of training signal.

Desired Qualifications

  • Experience with computational biology tools such as structure prediction, molecular docking, or virtual screening.
  • Experience training or evaluating machine learning models, particularly on molecular or biological data.
  • Publications in computational biology, bioinformatics, or molecular informatics.
  • Background in cheminformatics or molecular data analysis.
  • Experience working with protein or molecular language models.

About the Company

Output Biosciences

AI-driven preventative therapeutics for chronic disease

Output Biosciences develops preventative therapeutics to extend human healthspan by combatting chronic diseases. It uses computational biology and AI to discover therapies that can prevent diseases faster, with safer profiles and easier manufacturing, enabling quicker development timelines. The company differentiates itself by integrating biotech expertise with AI to create therapies focused on prevention, and by forming partnerships with pharmaceutical companies, licensing its technologies, and pursuing direct therapy sales. Its goal is to bring preventive treatments to market within months, shifting drug development toward stopping chronic diseases before they start.

Company size
1-10
Company stage
Seed
Total funding
$130K
Headquarters
New York City, New York
Founded
2020

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