Machine Learning Intern
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The role
Line 1: Join a cutting-edge ML team to advance protein sequence, structure, and function modeling during a 3-month summer internship (June–August 2026) in Somerville, MA.
Line 2: You will contribute to state-of-the-art research on generative modeling for proteins and therapeutic discovery, including LLM-based workflows.
Line 3: Work with a mentor to develop and implement a summer research project and deliver high-quality code within a cross-functional team.
Line 4: Project examples include building generative models of protein sequence and structure, modeling protein interactions, predicting biophysical properties, learning new protein representations, and applying generative designs to real drug programs.
Line 5: Key experiences include brainstorming with your mentor, presenting findings to the Computational Sciences group, delivering a capstone talk at the Summer Symposium, and engaging in mentor-led events.
Line 6: Ideal candidates are current PhD students in Applied Math, CS, Computational Biology, or Statistics, with experience in ML methods, modern DL frameworks (PyTorch, JAX, TF), Python (NumPy/SciPy), and protein data; familiarity with agentic LLM-enabled tools is a plus.
Line 2: You will contribute to state-of-the-art research on generative modeling for proteins and therapeutic discovery, including LLM-based workflows.
Line 3: Work with a mentor to develop and implement a summer research project and deliver high-quality code within a cross-functional team.
Line 4: Project examples include building generative models of protein sequence and structure, modeling protein interactions, predicting biophysical properties, learning new protein representations, and applying generative designs to real drug programs.
Line 5: Key experiences include brainstorming with your mentor, presenting findings to the Computational Sciences group, delivering a capstone talk at the Summer Symposium, and engaging in mentor-led events.
Line 6: Ideal candidates are current PhD students in Applied Math, CS, Computational Biology, or Statistics, with experience in ML methods, modern DL frameworks (PyTorch, JAX, TF), Python (NumPy/SciPy), and protein data; familiarity with agentic LLM-enabled tools is a plus.
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