Expedition Medicines | Cambridge, MA (Senior) Scientist, Machine Learning

The Role

Expedition is seeking a motivated and innovative (Senior) Scientist, Machine Learning to join our team. In this role, you will play a critical role in developing, evaluating, and applying machine learning approaches that connect Expedition’s proprietary chemoproteomics data with quantum chemistry, electronic structure, and generative molecular design. The successful candidate will combine strong expertise in modern machine learning with a deep understanding of molecular representation, quantum chemistry, and computational drug discovery to drive impact across Expedition’s drug discovery programs.

This individual will contribute to the advancement of a state‑of‑the‑art AI platform for covalent drug discovery, with a focus on linking large‑scale atom‑precision experimental data to physically meaningful features such as electronic structure, reactivity, and DFT‑derived descriptors. A key part of the role will be applying generative models directly to active drug discovery programs in close partnership with medicinal chemistry, while developing rigorous benchmarks to evaluate model performance and guide platform improvement.

The ideal candidate is highly collaborative, scientifically rigorous, comfortable with hands‑on data curation, and capable of independently driving projects in a fast‑paced research environment.

• 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 our 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 that 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 closely with computational, chemistry, biology, and proteomics teams to translate platform data into actionable models for discovery programs.
• Partner with engineering teams to product ionize modeling workflows, improve data infrastructure, and build self‑serve capabilities for chemistry and discovery teams.
• Communicate technical findings, model performance, and scientific implications clearly across cross‑functional teams.

• Ph.D. in machine learning, computational chemistry, chemical physics, computer science, applied mathematics, or a related discipline with 2+ years of industry experience, or M.S. degree with 6+ years of industry experience.
• Experience with quantum chemistry, DFT, electronic structure methods, or post‑DFT descriptors.
• Experience building molecular ML models including graph neural networks, geometric deep learning, equivariant architectures, diffusion models, or related approaches. Publications or preprints in, e.g., NeurIPS, ICML, ICLR, bioRxiv are a strong plus.
• 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 ML or scientific machine learning applications and an ability to curate, clean, integrate, and analyze complex, large‑scale scientific datasets from multiple sources.
• Proficiency with Python and modern ML frameworks such as PyTorch, PyTorch Geometric, DGL, or related tools and cheminformatics and molecular…