Our generative models learn the blueprint of life by modeling large-scale evolutionary data, enabling us to engineer and write biology in unprecedented ways. We are seeking a passionate Computational Research Intern to join moonshot projects at the intersection of bioinformatics and machine learning.
This is a rare opportunity to tackle open-ended scientific and engineering challenges with direct potential for high-impact publications and real-world applications in synthetic biology and protein engineering.
Responsibilities
- Design and implement computational approaches to advance cutting-edge research in bioinformatics and machine learning
- Analyze large-scale genomic and protein datasets to uncover new biological insights and capabilities
- Collaborate closely with scientists and engineers across machine learning, protein engineering, and biology teams
- Document and present research findings internally, with the potential to publish in leading journals and conferences
Qualifications
- Enrolled in a BS, MS, or PhD program in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field
- Strong programming skills in Python and familiarity with scientific computing libraries (e.g., NumPy, pandas, Jupyter)
- Experience in one or more of the following: machine learning, large-scale data analysis, genomics, or protein bioinformatics
- Excellent problem-solving skills, scientific curiosity, and ability to work both independently and collaboratively
Internship Details
- Typical duration: 12 weeks, with flexible start dates
- Hybrid: 2-3 days on-site per week in our Emeryville, CA headquarters
- Opportunity to publish your work and make a lasting impact in computational biology and AI-driven protein design
What We Offer at Profluent
- Work on moonshot projects at the frontier of AI and biology
- Mentorship from leading experts in bioinformatics, machine learning, and synthetic biology
- A dynamic, mission-driven environment with the resources of a fast-growing, well-funded startup
- Competitive internship compensation