Research Scientist - Computational Biologist

: A strong foundation in structural bioinformatics and AI/ML modeling, with proficiency in a programming language like Python, and the communication skills to work effectively in a cross-functional scientific environment.

protein design tools, such as diffusion models or physics-based approaches.

A track record of applying computational tools to solve biological problems, evidenced by publications or significant project contributions.

Familiarity with cloud computing environments (e.g., AWS, GCP) and MLOps principles.

Experience in antibody or protein engineering and a foundational understanding of immunology.

A proactive mindset with a passion for staying current with the rapidly evolving AI landscape and a motivation to test and evaluate new methodologies.

We offer a comprehensive benefits package focusing on financial, physical, and mental well-being while encouraging our employees to pursue our purpose! Some highlights include:

Multiple relocation packages

Two weeklong shutdowns (mid-summer and year-end) in the US (in addition to PTO)

8-week parental leave

9 Employee Resource Groups

Annual bonus offering

As a Research Scientist on our Discovery Research team, you will be at the forefront of Elanco’s mission to deliver innovative biologics that improve animal health.

You will leverage and develop state-of-the-art AI and structural modeling tools to predict, design, and optimize therapeutic antibodies and proteins.

Partnering closely with our principal Computational Scientist and wet-lab teams, you will directly impact the speed and success of our pipeline, helping to create the next generation of animal health therapies.

Utilize and develop AI-driven tools (e.g., AlphaFold, Rosetta) to predict and design high-resolution structures of antibodies and other proteins, directly supporting our therapeutic biologics pipeline.

Apply generative AI and structural modeling techniques to design novel antibody and protein modalities from scratch, optimizing them for high-precision epitope targeting, affinity, and specificity.

Proactively identify and engineer out potential liabilities (e.g., aggregation, immunogenicity) and train novel structure-based ML architectures to enhance the developability of our biologic candidates.