Senior Principal Scientist I, Lab Head, Protein Engineering

Sanofi•Cambridge, MA

12d•$148,500 - $214,500•Onsite

About The Position

As Senior Principal Scientist I, Lab Head, Protein Engineering within our R&D team, you will lead a multidisciplinary team within the Protein Engineering group at our Cambridge Crossing research site. In this role, you will collaborate closely with two complementary labs within the Protein Engineering group — Structural Biology & Protein Design and Developability & Analytics — to advance our protein engineering capabilities and drive innovation across the group. This leadership position offers a unique opportunity to shape the future of biologics engineering by combining deep experimental expertise with computational protein design, AI-enabled engineering, automation, and data-driven discovery. You will lead innovative antibody engineering strategies across diverse therapeutic modalities while working closely with other global LMR sites and LMR Digital Biologics. As Lab Head, you will help drive the evolution of Sanofi's next-generation antibody engineering platforms by integrating structure-guided design, high-throughput experimental technologies, protein language models, computational modeling, and AI/ML-enabled engineering approaches into biologics discovery workflows. You will represent Protein Engineering on multidisciplinary project teams, provide scientific leadership from early discovery through lead optimization, and contribute to the strategic direction of global engineering capabilities across LMR. Join the engine of Sanofi’s mission — where deep immunoscience meets bold, AI-powered research. In R&D, you’ll drive breakthroughs that could turn the impossible into possible for millions.

Requirements

Ph.D. in Biochemistry, Molecular Biology, Biomedical Sciences, Bioengineering, Structural Biology, or a related discipline
9+ years of industry experience in biotechnology or pharmaceutical research.
Demonstrated scientific leadership in antibody and protein engineering, including multispecific antibodies, Fc engineering, affinity maturation, and molecular optimization.
Extensive experience with yeast and/or phage display technologies, antibody library design and construction, high-throughput screening, protein expression, purification, protein-protein interaction measurement and biophysical characterization.
Strong understanding of protein structure-function relationships and engineering strategies to optimize affinity, specificity, biological activity, developability, and manufacturability.
Experience leveraging Genedata Biologics or comparable digital platforms to support biologics discovery and engineering workflows.
Demonstrated ability to integrate computational design and AI-enabled approaches into protein engineering programs through collaboration with computational scientists and digital biology teams.
Proven experience leading multidisciplinary project teams and managing a team of scientists in a collaborative research environment.
Strong scientific communication skills with a demonstrated record of publications, patents, scientific presentations, and innovation.

Nice To Haves

Experience applying modern computational protein design approaches, including protein language models, ML-guided engineering, generative AI, and structure prediction tools to biologics discovery.
Experience with computational modeling software such as MOE, Rosetta, AlphaFold, PyMOL, or related platforms.
Experience developing automated and high-throughput protein engineering workflows.
Experience in additional protein engineering disciplines including glycoengineering, chemical conjugation, protein conjugates, or emerging biologic modalities.

Responsibilities

Lead protein engineering strategies for the design and optimization of multispecific antibodies, Fc-engineered molecules, fusion proteins, and other next-generation biologic modalities using experimental and computational approaches.
Design and execute antibody engineering campaigns including affinity maturation, molecular format engineering, developability optimization, and functional optimization through structure-guided, computational, and data-driven design.
Develop and continuously improve protein engineering platforms utilizing yeast and/or phage display, high-throughput screening, saturation mutagenesis, in silico-guided library design, linear expression cassette (LEC) workflows and automation.
Partner closely with LMR Digital Biologics and global Protein Engineering community to integrate computational modeling, protein language models, machine learning, generative AI approaches, and experimental validation into engineering strategies.
Identify and implement innovative technologies that enhance engineering efficiency, data quality, and scientific decision-making across biologics discovery.
Serve as the Protein Engineering representative on cross-functional project teams, collaborating with Therapeutic Areas and partner functions to define engineering strategies, coordinate project activities, and support milestone progression.
Lead, mentor, and develop a high-performing team of 5 scientists (scientists to principal scientists) while fostering scientific excellence, innovation, collaboration, and continuous learning.
Contribute to global LMR Antibody Engineering initiatives by establishing best practices, sharing technologies across research sites, developing platform capabilities, and driving scientific innovation throughout the organization.
Communicate scientific discoveries through presentations, publications, patents, and external scientific engagement.