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AI Molecular Simulation Engineer

James Yamamoto

Full-time · Job · New_York

About the role

PyTorch, RDKit, and physics-informed neural networks are the backbone of what we build at Axion Labs. We develop ML models for molecular simulation — predicting binding affinities, conformational landscapes, and ADMET properties at scale. Our compute stack spans HPC clusters and cloud GPU infrastructure. In this role, you'll design graph neural network architectures for molecular representation, run large-scale training experiments, and collaborate directly with computational chemists to validate outputs. You'll also build the internal tooling that lets drug discovery scientists query predictions without touching raw model code. Every model you ship influences which compounds advance to preclinical evaluation. When your binding affinity predictor narrows a 10,000-compound library to 40 candidates, that is a direct contribution to a potential drug. We maintain strict reproducibility standards and expect all experiments to be fully logged and versioned.

Responsibilities

  • Design and train graph neural networks for molecular property prediction
  • Collaborate with medicinal chemists to validate model outputs against experimental data
  • Build and maintain internal APIs for model inference
  • Run large-scale hyperparameter search experiments on GPU clusters
  • Own experiment tracking and reproducibility tooling

Requirements

  • 5+ years ML engineering experience
  • Strong Python and PyTorch skills
  • Familiarity with molecular representations (SMILES, graphs)
  • Experience with large-scale distributed training
  • Background in cheminformatics or computational biology is a plus

Benefits

  • Fully remote with async-first culture
  • Equity package in a Series B biotech
  • $5,000 annual compute credits for personal research
  • Health, dental, and vision insurance
  • 4 weeks PTO plus flexible holidays

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Categories

AI & Machine Learning Engineers

Skills

python pytorch numpy tensorflow gcp

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Skills

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