2026 AIML Biotherapeutics Development Intern PhD

Company Description

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 at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

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

Qualifications

Minimum Qualifications

Currently enrolled in university, pursuing a PhD in 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 date between 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.

Additional Information

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 in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.​

Note : No amount of pay is considered to be wages 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 benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s 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.

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