2027 Spring Co op Biologics Design and AI Cambridge MA

Sanofi•Cambridge, MA

7d•Onsite

About The Position

Sanofi is seeking a highly motivated Spring Co-op Data Scientist to join its Biologics Design and AI team in Cambridge, Massachusetts. The six-month co-op focuses on bridging large language models (LLMs) and autonomous AI agents with biophysical protein prediction models. The co-op will work with computational biologists and AI scientists to develop agentic workflows that reason over structural data, orchestrate predictive tools, and map protein sequences to therapeutic properties, helping accelerate the biologics pipeline. The work includes adapting conversational LLMs to interface with specialized protein-prediction data. The co-op will use parameter-efficient methods such as LoRA and PEFT to help models understand protein sequences, structural embeddings, and biophysical constraints, and contribute to hybrid architectures for predicting properties such as antibody and nanobody binding affinity and thermal stability. The co-op will collaborate with experimental protein engineers and computational biologists to understand biological constraints, define agent objectives, and validate predictions. At the end of the spring term, the co-op will present methodologies, insights, and model performance to cross-functional R&D teams. The position is onsite and requires 40 hours per week, Monday through Friday, for the full co-op duration. Candidates must be enrolled in an accredited college or university throughout the co-op and able to relocate to the office location.

Requirements

Currently pursuing a Master’s or PhD in Computer Science, Data Science, Computational Biology, Bioinformatics, Bioengineering, or a related field, with plans to graduate by Spring 2027 or 2028.
Must be permanently authorized to work in the U.S. without requiring employment visa sponsorship at application or in the future.
Must remain enrolled in an accredited college or university throughout the co-op/internship.
Must be able to relocate to the office location and work 40 hours per week, Monday through Friday, for the full co-op duration.
Fluent in English.

Nice To Haves

Academic projects or industry experience applying NLP, LLMs, or agentic workflows to complex problems, ideally scientific ones.
Familiarity with biological data, especially protein sequences and 3D structures.
Basic domain knowledge in immunology, structural biology, or biophysics.
Strong Python programming skills and familiarity with standard data science libraries.
Hands-on experience with LLM fine-tuning methodologies and orchestration frameworks.
Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Experience using AI tools for biology, such as Protein LLMs, AlphaFold, or Rosetta.
Excellent problem-solving and communication skills, and an interest in generative AI and healthcare.

Responsibilities

Fine-tune conversational, natural-language LLMs to interface with and reason over data from specialized protein-prediction models.
Use parameter-efficient techniques such as LoRA and PEFT to adapt models to protein sequences, structural embeddings, and biophysical constraints.
Build hybrid architectures intended to improve predictions of antibody and nanobody properties, including binding affinity and thermal stability.
Work with experimental protein engineers and computational biologists to understand biological constraints and define agent objectives.
Validate model predictions.
Present methodologies, insights, and model performance to cross-functional R&D teams at the conclusion of the spring term.