2027 Spring Co op Biologics Design and AI Cambridge MA
About The Position
Sanofi is seeking a highly motivated Spring Co-op to join its Biologics Design and AI team. The role focuses on bridging large language models and autonomous AI agents with biophysical protein predictive models. The co-op will work with computational biologists and AI scientists to build workflows that reason over structural data, orchestrate predictive tools, and connect protein sequences to therapeutic properties, helping accelerate the biologics pipeline. The co-op will fine-tune conversational language models to interface with specialized protein prediction models and reason over protein sequences, structural embeddings, and biophysical constraints. Parameter-efficient methods such as LoRA and PEFT will be used to develop hybrid architectures for predicting antibody and nanobody properties, including binding affinity and thermal stability. The co-op will collaborate with experimental protein engineers and computational biologists to define objectives and validate predictions, and will present methodologies, insights, and model performance to cross-functional R&D teams at the conclusion of the spring term. This is a six-month, onsite co-op in Cambridge, Massachusetts. The co-op must be able to work 40 hours per week, Monday through Friday, for the full duration and relocate to the office location.
Requirements
- Currently pursuing a master's or PhD degree in Computer Science, Data Science, Computational Biology, Bioinformatics, Bioengineering, or a related field, with plans to graduate by Spring 2027 or 2028.
- Permanently authorized to work in the United States without requiring employment visa sponsorship at the time of application or in the future.
- Enrolled in an accredited college or university throughout the co-op or internship.
- Able to relocate to the office location and work 40 hours per week, Monday through Friday, for the full duration of the co-op.
- 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, particularly protein sequences and 3D structures.
- Basic domain knowledge in immunology, structural biology, or biophysics.
- Strong programming skills in Python and 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 state-of-the-art AI tools for biology, such as Protein LLMs, AlphaFold, or Rosetta.
- Excellent problem-solving and communication skills, with a passion for working at the intersection of generative AI and healthcare.
Responsibilities
- Fine-tune conversational, natural language large language models to interface with and reason over data from specialized protein prediction models.
- Use parameter-efficient techniques such as LoRA and PEFT to adapt language models to protein sequences, structural embeddings, and biophysical constraints.
- Build hybrid architectures to improve predictions of antibody and nanobody properties, including binding affinity and thermal stability.
- Collaborate 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.
Benefits
- Mentoring and guidance from inspirational leaders.
- Apprenticeships, internships, graduate programs, and international experiences.
- Access to the latest tools, digital innovation, and continuous learning.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
