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Output Biosciences

AI-driven preventative therapeutics for chronic disease

Member of Technical Staff - Generative

Translational Research & Bench BiologyConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • You have 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 generative modeling.
  • You have a strong publication record in generative methods at top-tier venues such as NeurIPS, ICML, or ICLR.
  • You have extensive hands-on experience designing, building, and training deep generative models, including work on novel architectures, training objectives, or sampling methods.
  • You are proficient in Python and PyTorch, and have experience training models on distributed multi-GPU infrastructure.
  • You have demonstrated the ability to own the full research-to-training pipeline, including designing methods, training models, and shipping them.
  • You write production-quality code that is well-tested and maintainable, and are comfortable working in shared codebases with version control and code review.
  • You are a rigorous experimentalist who designs evaluations carefully, tracks experiments systematically, and draws conclusions from data.

Responsibilities

  • Design and build generative architectures for molecular data spanning multiple modalities, including small molecules, peptides, mini proteins, and more.
  • Develop training approaches that learn from diverse biological signal to ensure the model composes genuinely novel structures.
  • Build methods for controllable, targeted generation, enabling the model to produce molecules with specified biological properties while satisfying real-world chemical constraints.
  • Integrate biological reasoning from the foundation model into the generative pipeline, using learned biological representations to guide and condition generation.
  • Own training end-to-end, including experiment design, distributed training on multi-GPU clusters, hyperparameter optimization, and iteration.
  • Design evaluation frameworks beyond statistical metrics to measure whether generated molecules are biologically meaningful, structurally valid, and genuinely novel.
  • Own day-to-day management of the work and achieve milestones.

Desired Qualifications

  • Experience applying generative models to molecular, chemical, or biological data.
  • A background in chemistry, biology, computational biology, biophysics, or a related natural science.
  • Experience with multi-modal learning or cross-modality translation.
  • Experience with conditional or controllable generation methods.
  • 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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