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Expedition Medicines

AI-driven small-molecule drug design

Senior Machine Learning Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeCambridge, MA, USAIn-person

About the Job

Requirements

  • A Ph.D. in machine learning, computational chemistry, chemical physics, computer science, applied mathematics, or a related discipline with 2+ years of industry experience, or an M.S. degree with 6+ years of industry experience.
  • Experience with quantum chemistry, density functional theory (DFT), electronic structure methods, or post-DFT descriptors.
  • Experience building molecular machine-learning models, including graph neural networks, geometric deep learning, equivariant architectures, diffusion models, or related approaches.
  • Experience applying generative models, molecular design models, or active learning workflows to drug discovery or chemistry optimization problems.
  • Experience working closely with medicinal chemistry teams to prioritize compounds, interpret model outputs, and incorporate experimental feedback into model development.
  • Experience developing rigorous model evaluation frameworks, benchmarks, and validation strategies for molecular machine learning or scientific machine learning applications, and ability to curate, clean, integrate, and analyze complex, large-scale scientific datasets from multiple sources.
  • Experience with scalable data processing, model training, and analysis workflows for large scientific datasets.
  • Ability to work closely with experimental scientists and translate biological and chemical questions into computational strategies.
  • Excellent communication and cross-functional collaboration skills.

Responsibilities

  • Develop, implement, and evaluate innovative machine-learning methods for connecting (macro-)molecular quantum chemical features, reactivity modeling, covalent bond formation, and proteome-wide target engagement data.
  • Design and implement rigorous benchmarks to evaluate model performance, including retrospective, prospective, and program-relevant validation strategies.
  • Refine, fine-tune, and apply Expedition’s foundational models to drug discovery programs in close partnership with medicinal chemistry teams, supporting compound design, prioritization, and iterative learning from experimental results.
  • Perform hands-on data curation, quality control, and dataset construction to ensure models are trained and evaluated on high-quality, biologically and chemically meaningful data.
  • Develop scalable featurization and modeling pipelines for large molecular datasets, including quantum chemistry outputs, conformer ensembles, protein-ligand interaction data, covalent reactivity data, and experimental chemoproteomics data.
  • Collaborate with computational, chemistry, biology, and proteomics teams to translate platform data into actionable models for discovery programs.
  • Partner with engineering teams to productionize modeling workflows, improve data infrastructure, and build self-serve capabilities for chemistry and discovery teams.
  • Communicate technical findings, model performance, and scientific implications across cross-functional teams.

Desired Qualifications

  • Publications or preprints in venues such as NeurIPS, ICML, ICLR, or bioRxiv are a strong plus.
  • Proficiency with Python and modern machine-learning frameworks such as PyTorch, PyTorch Geometric, DGL, or related tools, and cheminformatics and molecular modeling toolkits such as RDKit, ORCA, Gaussian, Q-Chem, or related software is preferred.
  • Experience with covalent chemistry, reaction modeling, structure-based design, or chemoproteomics data is a plus.

About the Company

Expedition Medicines

AI-driven small-molecule drug design

Expedition Medicines designs small molecule drugs for hard-to-drug protein targets using AI and quantum chemistry. Its platform blends quantum-chemistry analysis with chemoproteomics to pursue a reaction-first, covalent binding strategy rather than a traditional binding-first approach. It differentiates itself by applying covalent and generative chemistry with AI-driven design and by pursuing major pharma collaborations, such as a Pfizer multi-target exploration agreement. Its goal is to build a pipeline of first-in-class therapies in oncology and immunology and to advance drug candidates into preclinical and clinical development through internal programs and partnerships.

Company size
51-200
Company stage
Early VC
Total funding
$50M
Headquarters
Cambridge, MA
Founded
2022

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