(Senior) Scientist, Machine Learning Full Time NEW

COMPANY DESCRIPTION

Expedition Medicines is a privately held, early-stage biotechnology company pioneering the emerging field of Protein Editing. At Expedition Medicines we create small molecules that edit protein structure and function to unlock presently undruggable targets and a broad array of therapeutic modalities. Our platform integrates novel small molecule chemistry and chemoproteomic discovery technologies with Machine Learning (ML) to enable generative design. Expedition Medicines is backed by Flagship Pioneering, bringing their courage, vision, and resources to guide Expedition Medicines from platform validation to patient impact. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us! 

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.

KEY 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

PROFESSIONAL EXPERIENCE & QUALIFICATIONS

• 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 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 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

LOCATION: Cambridge, MA

ABOUT FLAGSHIP PIONEERING

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

The salary range for this role is $132,000 – $258,500. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Expedition Medicines currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Expedition Medicines’s good faith estimate as of the date of publication and may be modified in the future.

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