What are the responsibilities and job description for the Intern, Machine Learning (Summer 2025) position at BigHat Biosciences?
Intern, Machine Learning (Summer 2025)
The Role: We are seeking talented, hard working interns to join our Machine Learning team for Summer 2025.
At BigHat Biosciences we frame antibody drug development as an iterative multi-objective optimization problem with a wet lab in-the-loop. A high-throughput weekly build-train-test cycle lets us rapidly evaluate and deploy a broad range of generative and predictive models on real-world therapeutics design challenges. Active learning and Bayesian optimization methods let us ensure these experiments also intelligently gather training data to continuously improve our models.
As an aspiring machine learning engineer or scientist you’ll be mentored by a member of our ML team and work with an interdisciplinary team of scientists to develop new methods to solve important ML-driven protein engineering problems.
Job Responsibilities
- Develop and evaluate a novel ML modeling or sequence optimization approach to solve an antibody engineering challenge relevant to BigHat’s therapeutics programs.
- Work with an interdisciplinary team of biologists, data scientists and machine learning scientists to gain sufficient domain familiarity to ensure your work is impactful.
- Document and present your results to relevant BigHat departments.
- Write production-grade code such that successful methods can be deployed and evaluated in the wet lab.
Preferred Qualifications
- PhD in progress, BS/MS degree in ML, CS or in the sciences with significant ML experience and a strong mathematical background. Truly exceptional undergraduates may be considered.
- Strong competency in Python, familiarity with PyTorch, exposure to modern software engineering best practices.
- Strong communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
- Nice-to-haves include experience with protein structure modeling, familiarity with antibody biology, and experience training and deploying models on AWS.
