Intern - Research - Drug Metabolism - AI

Gilead Sciences•Foster City, CA

5d•$20 - $55•Hybrid

About The Position

As a Gilead intern you will contribute to high-impact meaningful projects that will not only further advance our company’s mission but will allow you to gain real world experience at one of the most innovative organizations in the world. You will also have opportunities to participate in special events including professional development and leadership presentations, social/network building activities and local community volunteer programs.

Requirements

Must be at least 18 years of age at the start of the internship.
Must be enrolled full‑time in a Bachelor’s, Master’s, MBA, JD or PhD program at a nationally accredited U.S. college or university, with continued full‑time enrollment required in the Fall semester following the internship.
Must be currently enrolled as an undergraduate (freshman, sophomore, junior, or senior) or as a graduate/doctoral student.
Must have a minimum overall cumulative GPA of 2.8 at the time of application and hire (official transcripts will be requested).
Must be independently authorized to work in the United States now and in the future (current or future work authorization must not require school or employer sponsorship) official transcripts are required.
Must be able to commit to a full‑time internship lasting 10–12 consecutive weeks between May and August.
Must be willing and able to work at the designated site for the duration of the internship (relocation may be required). All internships are hybrid; fully remote roles are not offered.

Nice To Haves

Preferred degree level: Graduate / PhD
Preferred majors: Working toward a Bachelor's, Master's, or Ph.D. degree in Computer Science, Physics, Mathematics, Statistics, Data Science, or a related AI-focused field
Hands-on experience in Python, machine learning, statistical modeling, or generative AI tools
Ability to collaborate with scientists and translate research questions into data-driven or AI-enabled approaches
Demonstrated strength in AI/ML development, including programming with Python, R, MATLAB; model development and evaluation; generative AI; large language models; retrieval-augmented generation; and AI workflow or agent development.
Experience with reproducible scientific computing, version control, data visualization, and documentation of analytical workflows, with familiarity using Visual Studio Code, Git, GitHub, and AI-assisted development tools such as GitHub Copilot or Claude Code.
Experience working with complex scientific datasets. Familiarity with LC-MS, mass spectrometry, proteomics, bioanalytical data, pharmacokinetics, PK/PD modeling, or simulation is preferred.
Ability to communicate technical AI concepts clearly and work with scientific subject-matter experts to develop fit-for-purpose solutions.
Proficiency with Microsoft Office (MS Office) tools, such as Word, Excel, PowerPoint, and Outlook
Strong problem‑solving skills, with the ability to identify issues and proactively seek solutions
Ability to work effectively both independently and as part of a team in a collaborative environment
Demonstrated commitment to inclusion and diversity, including the ability to work respectfully with individuals from diverse backgrounds
Highly organized, detail‑oriented, and able to manage competing priorities in a fast‑paced environment with short timelines

Responsibilities

Apply artificial intelligence and machine-learning approaches to enhance scientific data analysis, interpretation, workflow efficiency, and decision support across DMPK applications.
Develop, evaluate, and document reproducible AI-enabled workflows and AI agents, including large language model applications, retrieval-augmented generation, tool-enabled or agentic workflows, and programmatic analytical pipelines for scientific data and research use cases.
Collaborate with DMPK, bioanalytical, LC-MS, and quantitative scientists to translate scientific questions and workflow needs into practical, data-driven or AI-enabled solutions.
Analyze representative DMPK datasets and evaluate workflow accuracy, robustness, reproducibility, efficiency, and scientific utility.
Initial applications will emphasize LC-MS, with potential applicability to PK/PD modeling, simulation, and project-support workflows.
Document code, methods, model assumptions, testing results, limitations, and recommendations to support transparent and responsible use of AI-enabled scientific workflows.
Showcase your work with a final presentation near the conclusion of your internship