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AI Protein Structure Function Predictor

Wyatt Tanaka

Full-time · Job · New_York

About the role

AlphaFold told us what a protein looks like. What it doesn't tell us is what the protein does. Structure-to-function prediction — given a folded protein structure, predict its enzymatic activity, binding partners, subcellular localization, and disease associations — is the next major challenge in computational biology, and the one most directly relevant to drug discovery. We partner with 12 pharmaceutical companies on target identification and lead optimization. Our structure-to-function models are used to prioritize which protein variants to synthesize and test in the lab, reducing the experimental search space from thousands of variants to dozens. That means our false negatives cost months and millions of dollars. You'll lead structure-to-function prediction modeling: designing architectures that extract function-relevant features from 3D protein structures, integrating multi-modal signals from sequence, evolutionary conservation, and structural context, and building the uncertainty quantification that tells drug hunters how confident to be in each prediction. The role is hybrid — 2 days per week in our Cambridge MA office for wet lab collaboration with the biology team.

Responsibilities

  • Design structure-to-function prediction models integrating 3D structural and evolutionary sequence features
  • Build multi-modal fusion architectures combining sequence embeddings with structural graph representations
  • Implement uncertainty quantification providing calibrated confidence on function predictions
  • Validate models against experimental assay results with wet lab biology collaborators
  • Develop active learning pipeline prioritizing protein variants for experimental synthesis
  • Publish methodology in structural bioinformatics or computational biology venues

Requirements

  • 7+ years in computational biology, bioinformatics, or ML for drug discovery
  • Deep expertise in protein structure representation learning and geometric deep learning
  • Strong Python and PyTorch skills with experience using AlphaFold or ESM-family embeddings
  • Understanding of protein biochemistry sufficient to evaluate prediction validity
  • Track record of ML models that translate to experimental outcomes

Benefits

  • Hybrid: 2 days Cambridge MA, 3 days remote
  • Top-of-market compensation for computational biology ML expertise
  • Research publication budget
  • Comprehensive health benefits
  • Equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn spark statistics

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Salary Range

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Skills

python pytorch scikit-learn spark statistics pandas

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