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Output Biosciences

AI-driven preventative therapeutics for chronic disease

Member of Technical Staff - Interpretability

AI ResearchConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of post-doctoral or industry research experience, or a Bachelor's or Master's degree with 5+ years of hands-on research and engineering experience in model interpretability or representation analysis.
  • A strong publication record at top-tier venues such as NeurIPS, ICML, or ICLR, with contributions to mechanistic interpretability, representation analysis, probing methods, or model understanding.
  • Hands-on experience analyzing the internal representations of large neural networks and designing experiments that reveal what models have learned.
  • Proficiency in Python and PyTorch, and experience working with large models on GPU infrastructure.
  • Demonstrated ability to take interpretability research from experiments to usable tools, including building systems others can use.
  • Ability to write production-quality, well-tested, maintainable code and work in shared codebases with version control and code review.
  • Ability to evaluate evidence that a model has learned a concept and design experiments that distinguish real capabilities from artifacts.

Responsibilities

  • Continue developing methods for probing and reverse-engineering the model's learned representations and understanding how it encodes biological information across molecular scales.
  • Design and run experiments to identify and characterize capabilities, mapping what the model has learned about molecular interactions and biological function.
  • Build methods to extract the model's biological understanding as explicit, usable outputs that downstream systems and researchers can act on.
  • Create tools that connect model internals to meaningful biological concepts, making the model's reasoning interpretable to scientists and useful in practice.
  • Work closely with the pretraining and generation teams, feeding interpretability findings back into model development to strengthen the capabilities uncovered.
  • Own the full pipeline from probing experiments to production-quality interpretability tools, building robust systems on distributed infrastructure.

Desired Qualifications

  • A background in chemistry, biology, computational biology, biophysics, or a related natural science.
  • Experience interpreting machine learning models trained on scientific or biological data.
  • Experience building visualization or analysis tools for model internals.
  • Experience with multimodal models or representations that span multiple data types.
  • Contributions to open-source machine learning projects.

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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