Job Description

Title: Machine Learning Scientist
Location: Boston, MA
Salary: $160,000-$200,000

EPM Scientific is proud to partner with a cutting-edge biotech company applying machine learning to protein engineering and therapeutic design. As a Machine Learning Scientist , you'll work closely with a multidisciplinary team of ML researchers, structural biologists, immunologists, and protein engineers to develop innovative models that optimize natural proteins and design synthetic proteins with novel therapeutic functions.

This is a unique opportunity to apply your expertise at the intersection of AI and biology, contributing directly to the development of next-generation biologic drugs.

• Design and train machine learning models to optimize natural proteins for therapeutic properties and engineer synthetic proteins with novel functions.
• Lead computational efforts in de novo antibody design targeting challenging antigens.
• Integrate diverse datasets-including sequence, structure, function, and molecular characterization-to enable multi-objective optimization of protein properties.
• Collaborate with wet lab scientists to translate in-silico designs into real-world therapeutic candidates.
• Build robust data infrastructure to support model training, analysis, and visualization.
• Develop production-quality code for use across computational and experimental teams.
• Communicate scientific progress through presentations, reports, and internal/external documentation.

• A rigorous ML scientist with deep understanding of molecular biology, protein biochemistry, and structure.
• Experienced in protein design using sequence-based models (e.g., evolutionary models, LLMs) and/or structure-based models (e.g., diffusion, inverse folding).
• Skilled at communicating complex computational concepts to diverse audiences.
• Comfortable working with wet lab teams to bridge computational and experimental workflows.
• Driven, collaborative, and passionate about improving patients' lives through science.

• Ph.D. in Mathematics, Computational Biology, Computer Science, Statistics, or a related field. Candidates with strong computational experience and a Ph.D. in a non-computational field will also be considered.
• 3+ years of industry experience applying ML to biologic drug development, including deep learning models such as VAEs and GNNs.
• Hands-on experience in protein engineering, statistical genetics, or related areas.
• Proven track record of collaboration with experimental scientists in pharmaceutical settings.
• Proficiency in Python and data analysis tools (e.g., NumPy/SciPy, R).

• Experience with cloud computing platforms (AWS, Google Cloud).
• Familiarity with interactive app frameworks (Dash, R Shiny).
• Background in genomics or immunology.
• Publications applying ML to molecular or structural biology in major scientific journals.

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