Principal Scientist - Protein MIL Job Boston area,Massachusetts USA,Research/Development

Lead computational biology and machine learning efforts supporting protein discovery, engineering, and characterization.
Develop, fine-tune, and benchmark deep learning and foundation models for protein structure prediction, protein design, and antibody/binder discovery.
Build predictive models for protein properties including stability, aggregation, expression, binding affinity, and develop ability.
Design and execute computational protein and antibody design campaigns and integrate results with experimental validation cycles.
Develop scalable computational pipelines for antibody discovery, protein characterization, sequencing, proteomics, and other biological datasets.
Build data platforms, databases, APIs, and visualization tools that enable researchers to efficiently access and utilize complex biological data.
Collaborate with experimental scientists to design studies, analyze results, and integrate computational approaches into laboratory workflows.
Manage cloud, GPU, and high-performance computing environments supporting machine learning and large-scale biological analysis.
Lead, mentor, and develop a small team of computational biologists and bioinformaticians.
Establish best practices for data management, reproducibility, version control, model deployment, and MLOps.
Communicate computational strategies and findings to both technical and non-technical audiences and contribute to publications, intellectual property, and product development.

Requirements

PhD in computational biology, bioinformatics, biophysics, machine learning, or a related field, with strong experience in protein science or biochemistry.
5+ years of relevant industry or research experience, including technical leadership or experience mentoring/managing computational scientists.
Strong Python programming skills and hands-on experience with deep learning frameworks such as PyTorch.
Demonstrated experience developing, modifying, training, or applying machine learning models for protein structure prediction and/or protein design.
Experience working with multimodal biological datasets, including sequence, structure, assay, sequencing, or proteomics data.
Proven experience with de novo protein, binder, or antibody design and experimental validation.
Strong understanding of computational protein science and the ability to translate research methods into practical tools and workflows.