Associate Machine Learning Chemistry Jobs in Massachusetts

As a Senior Scientist in Computational Chemistry, you will help drive innovation in how we design and optimize drug candidates. You will independently design and implement computational approaches that directly influence project direction from hit identification through clinical candidate nomination.

You will apply best practices from machine learning approaches that integrate with physics-based methods to accelerate the design cycle for therapeutic programs. You will join a team of highly skilled computational scientists eager to drive projects while developing and applying new tools and methods with exceptional scientific rigor.

This is an exciting opportunity to join a growing computational chemistry team with the passion, resources and mandate to build a world-class group.

Responsibilities

• Design and execute molecular modeling, docking, and structure analysis to guide chemistry synthesis priorities and support structure-based drug design efforts for therapeutic projects.

• Collaborate closely with medicinal chemists, biologists and structure biologists to design compounds that address potency, selectivity, and developability objectives.

• Apply physics-based methods (molecular dynamics, free energy calculations) alongside machine learning approaches to improve models and accelerate the design-make-test-analyze cycle for lead optimization.

• Contribute to the development of computational workflows and software that enables rapid analysis of compound designs across multiple programs.

• Stay current with advances in computational chemistry and machine learning for drug discovery to continuously improve methodologies and approaches.

Required Qualifications & Skills

• PhD in Computational Chemistry, Biophysics, Medicinal Chemistry, or a related field with substantial industry experience (4+ years) and demonstrated independence of research projects in a cross-functional drug discovery environment.

• Level will be determined based on demonstrated independence, technical depth, project ownership, and ability to influence in a cross-functional drug discovery environment.

• Strong experience in structure-based drug design, including molecular modeling, molecular dynamics simulations, free energy calculations, protein-ligand docking, and structure analysis to support lead optimization efforts.

• Demonstrated ability to work collaboratively in cross-functional drug discovery teams and clearly communicate computational results to experimental scientists.

Preferred Qualifications

• Understanding of medicinal chemistry principles, structure-activity relationships, and the factors that influence compound optimization toward clinical development.

• Experience working with structural biology data (X-ray crystallography, cryo-EM) and translating structural insights into design hypotheses.

• Experience with AI toolkits for drug discovery to apply machine learning, multi-agent systems or active learning approaches to improve compound designs.

• Programming proficiency in Python and experience with machine learning frameworks (scikit-learn, PyTorch, TensorFlow, or similar).

• Experience with cloud computing platforms (AWS) and Linux environments.

Company benefits: Comprehensive medical, dental, vision, prescription drug coverage, company provided basic life, accidental death & dismemberment, short-term and long-term disability insurance, tuition reimbursement, student loan assistance, a generous 401(k) match, flexible time off, paid holidays, and paid leave programs as well as other company provided benefits.

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