2027 Future Talent Program - Data, AI, and Genome Sciences - Intern
About The Position
The Future Talent Program within the Research & Development Division’s Data, AI, and Genome Sciences (DAGS) Department offers internships lasting 10–12 weeks. There is one open position on the AI/ML Data Science, In Silico Perturbation Team. Interns may contribute to one or more projects, developing their skills and evaluating the company as a potential long-term career fit. The AI/ML Data Science projects involve developing statistical and computational methods and pipelines to use, analyze, interpret, and validate perturb-seq datasets, as well as developing approaches to assess, interpret, and validate in silico perturbation predictions from single-cell foundation models. The position is based in Cambridge, Massachusetts, and is full-time for 10–12 weeks beginning in May or June 2027. The work arrangement is hybrid.
Requirements
- Currently enrolled in a PhD degree program in Computer Science, Statistics, Bioinformatics, Computational Biology, or a related technical field.
- Available to work full-time for 10–12 weeks beginning in May or June 2027.
- Demonstrated experience with single-cell omics data, such as scRNA-seq or perturb-seq.
- Excellent software design and development skills and strong proficiency in Python.
- Excellent communication skills and ability to work collaboratively in a multidisciplinary team.
Nice To Haves
- Motivation to impact biological projects and independent project leadership experience.
- Experience with biological foundation models, such as single-cell foundation models.
- Experience with advanced statistics and/or Bayesian methods.
- Proficiency in modern machine learning frameworks, such as PyTorch, and experience implementing scalable solutions for complex tasks.
- Publications in journals or conferences.
Responsibilities
- Develop statistical and computational methods and pipelines to use, analyze, interpret, and validate perturb-seq datasets.
- Develop approaches to assess, interpret, and validate in silico perturbation predictions from single-cell foundation models.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
