AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas immunology, oncology, neuroscience, and eye care and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us atwww.abbvie.com. Follow @abbvie onX,Facebook,Instagram,YouTube,LinkedInandTik Tok.

AI/ML Biotherapeutics Development Internship Overview

Envision spending your summer working with energetic colleagues and inspirational leaders, all while gaining world-class experience in one of the most dynamic organizations in the pharmaceutical industry. This is a reality for AbbVie Interns.

The Quantitative, Translation & ADME Sciences (QTAS) team in South San Francisco supports the characterization, modeling the ADME (Absorption, Distribution, Metabolism, and Excretion) aspects of drug development research, part of which focuses on enhancing biologics property prediction through innovative AI/ML methods. This internship involves exploring explicit structural data integration into deep learning models for biologics sequence-to-property prediction, tapping into dynamic protein conformations through ensemble structures.

Key responsibilities include:

• Evaluate and analyze structures of biologics.

• Explore and implement state-of-the-art techniques to improve AI/ML models.

• Collaborate with AI/ML and computational biology experts to refine modeling strategies and achieve predefined success metrics

MinimumQualifications

• Currently enrolled in university, pursuing aPhDin Computer Science, Computational Biology, Biophysics, or other related field.

• Must be enrolled in university for at least one semester following the internship.

• Demonstrated experience with deep learning concepts and experience with Python and PyTorch.

• Familiarity with protein language models.

• Familiarity with protein structure data (e.g., PDB files) and structural biology concepts.

• Excellent communication, problem-solving, and collaboration skills.

Preferred Qualifications

• Expected graduation datebetween December 2026 June 2027.

• Antibody-related knowledge.

• Experience with molecular dynamic simulations or other protein structural sampling techniques.

• Experience with Graph Neural Network (GNN) models.

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:

Benefits and Amenities:

• Competitive pay

• Relocation support for eligible students

• Select wellness benefits and paid holiday / sick time

• The compensation range described below is the range of possible base pay compensation that the Company believes ingood faith it will pay for this role at the time of this posting based on the job grade for this position. Individualcompensation paid within this range will depend on many factors including geographic location, and we may ultimatelypay more or less than the posted range. This range may be modified in the future.

Note: No amount of payis considered to bewages or compensation until such amount is earned, vested, and determinable.The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefitsthat are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid andmay be modified at the Companys sole and absolute discretion, consistent with applicable law.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.

US & Puerto Rico only - to learn more, visithttps://www.abbvie.com/join-us/equal-employment-opportunity-employer.html

US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:

https://www.abbvie.com/join-us/reasonable-accommodations.html