Data Science Intern (US Remote)- Genomics Research

GreenLight Biosciences is seeking a highly motivated intern (US Remote) to join the Data Science team to take part in innovating RNAi by using cutting-edge advancements in GenAI technologies. The ideal intern will develop domain-specific Foundation and Large Language Models (LLMs) to enhance discovery pipelines. The position provides hands-on-experience in agricultural biotechnology under close mentorship, while contributing to real-world projects.

Program Highlights

· 12-week paid internship (full time, 40 hours per week), June to August (with possibility of 3 month extension).

· Hands-on experience with deep learning and LLMs in a real-world, domain-specific application.

· Ownership of challenging and impactful business-critical projects.

· Collaborate with talented people in the agricultural biotechnology industry.

Key Responsibilities

· Aggregate and integrate diverse RNAi datasets (public and internal) across multiple species.

· Contribute to cutting-edge research on foundation models and LLMs and their application in RNAi target genes and dsRNA designs.

· Train and finetune foundation transformer-based models to predict species-specific RNAi targets and triggers.

· Analyze system performance and contribute to iterative improvements through experimentation and testing.

· Effectively communicate findings through verbal presentations and impactful reports at internal team and stakeholder’s meetings.

Preferred Qualifications

· Currently pursuing or recently completed Ph.D. student in genomics, bioinformatics, computational biology, computer science, or a related field with a strong understanding of machine learning, artificial intelligence, or computational theory.

· Good fundamental knowledge in RNA biology, gene regulation, and RNA interference

Required Skills:

· Strong domain knowledge in cellular and molecular biology, preferably in non-human systems.

· Experience with Transformers, LLMs, and foundation models for biological data.

· Proficiency in self-supervised learning, transfer learning, and multi-task learning for RNA-related tasks.

· Strong coding skills in Python and deep learning frameworks such as PyTorch, Keras, or TensorFlow.

· Excellent communication and teamwork skills, with the ability to present findings effectively.

· Strong ability to work both independently and collaboratively in a research-driven environment.

Preferred Knowledge, Skills, and Qualifications

· Experience with AI agents, fine-tuning methods, prompt engineering, and LLM optimization techniques for biological text and sequence data.

· Demonstrated alignment with core values: Integrity, Courage, and Passion.