2027 Spring Co op Biologics Design and AI Cambridge MA

Job Title:2027 Spring Co-op Biologics Design and AI

• Grade:Co-op
• Location:Cambridge, MA
• Duration:6 months

About the Job

Looking to launch your career at the cutting edge of healthcare? Join Sanofi for a chance to develop with mentoring and guidance from inspirational leaders while helping to make an impact on the lives of countless people worldwide. We are seeking a highly motivatedSpring Co-opto join ourBiologics Design and AI team.

Sanofi is seeking a highly motivated and intellectually curious Data Scientist Spring Co-op to join our Biologics and Design AI team. In this role, you will be at the forefront of a highly innovative project: bridging the world of Large Language Models (LLMs) and autonomous AI agents with biophysical protein predictive models.

Biologics like antibodies and nanobodies are highly effective therapies but optimizing them is a complex challenge. You will closely work with computational biologists and AI scientists to build intelligent, agentic workflows that can reason over structural data, orchestrate predictive tools, and map protein sequences to critical therapeutic properties, ultimately accelerating our biologics pipeline.

Ready to grow your skills while helping shape the future of healthcare? At Sanofi, you’ll be empowered to learn, ask questions, and bring your ideas to life – all while supported by inspiring mentors and collaborative teams.

About Sanofi

We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

Main Responsibilities

• LLM Fine-Tuning & Cross-Modal Adaptation:Fine-tune conversational, natural language LLMs to directly interface with and reason over data from specialized protein prediction models. Using parameter-efficient techniques (e.g., LoRA, PEFT), you will adapt these human-language models to comprehend protein sequences, structural embeddings, and biophysical constraints. By bridging the gap between human-like generative AI reasoning and rigorous structural biology, you will build hybrid architectures designed to significantly enhance the accuracy of critical antibody and nanobody property predictions, such as binding affinity and thermal stability.
• Collaboration & Reporting:Partner closely with experimental protein engineers and computational biologists to understand biological constraints, define the agent's objectives, and validate model predictions, ultimately presenting your methodologies, insights, and model performance to cross-functional R&D teams at the conclusion of the spring term.

About you

Basic Qualifications:

• Currently pursuing a Master’s or PhD degree in Computer Science, Data Science, Computational Biology, Bioinformatics, Bioengineering, or related field with plans to graduate by Spring 2027 (or 2028).
• Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or STEM OPT usually require future sponsorship for long term employment and do not meet the Internal requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship.
• Must be enrolled in an accredited college or university throughout the duration of the co-op/internship.
• Must be able to relocate to the office location and work 40hrs/week, Monday to Friday for the full duration of the co-op/internship.
  

Preferred Qualifications:

• Previous academic projects or industry experience demonstrating the application of NLP, LLMs, or agentic workflows to complex, ideally scientific, problems.
• Familiarity with biological data, specifically protein sequences and 3D structures.
• Basic domain knowledge in immunology, structural biology, or biophysics.
• Strong programming skills inPythonand standard data science libraries.
• Hands-on experience with LLM fine-tuning methodologies and orchestration frameworks.
• Experience with deep learning frameworks such asPyTorch, TensorFlow, or JAX.
• Experience using state-of-the-art AI tools for biology (e.g., Protein LLMs, AlphaFold, Rosetta).
• Excellent problem-solving communication skills, with a passion for working at the intersection of generative AI and healthcare.
• Fluent in English.

Why Choose Us

• Bring the miracles of science to life alongside a supportive, future focused team.​
• Stretch your skills through apprenticeships, internships, graduate programs, and international experiences that grow with you.​
• Learn from inspiring mentors and collaborative teams while contributing to real projects that shape the future of healthcare.​
• Build your future with access to the latest tools, digital innovation, and continuous learning that keep you ahead of the curve.​​

Compensation

The salary range for this position is$47 - $60hourly. All compensation will be determined commensurate with demonstrated experience.

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Pursueprogress, discoverextraordinary

Better is out there. Better medications, better outcomes, better science. But progress doesn’t happen without people – people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let’s be those people.

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US and Puerto Rico Residents Only

Sanofi Inc. and its U.S. affiliates are Equal Opportunity employers committed to a culturally inclusive workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.