About the job
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.
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 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 productionize 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
Qualifications
Minimum
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.
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
Preferred
Publications or preprints in, e.g., NeurIPS, ICML, ICLR, bioRxiv a strong plus.
Proficiency with Python and modern ML 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 scalable data processing, model training, and analysis workflows for large scientific datasets
Experience with covalent chemistry, reaction modeling, structure-based design, or chemoproteomics data is a plus
Ability to work closely with experimental scientists and translate biological and chemical questions into computational strategies
Excellent communication and cross-functional collaboration skills