Intern – Research – Protein Therapeutics, Computational Biologics Engineering

Gilead Sciences•Foster City, CA

11h•$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. This opportunity will involve exploring how emerging generative protein design approaches can be applied to biologics discovery and development. The intern will contribute to the design and evaluation of antibody- and miniprotein-based therapeutic candidates while helping develop computational workflows for candidate generation, prioritization, and analysis. Research activities may include a combination of AI/ML methods, molecular modeling, structure-based design, and integration of experimental feedback to guide future design cycles.

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 Major: Computer Science, Biochemistry, Physics, Chemistry or Computational Biology
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
Proficient in Python for data analysis; experience with PyTorch or TensorFlow or similar libraries for model fine-tuning is a plus.
Experience in customizing AI/ML and physics-based de novo protein design workflows, including HPC job submission.
Demonstrated familiarity with structure-based molecular modeling tools (e.g., Chimera, PyMOL) and principles of structure-guided protein design.
Experience with or interest in antibody engineering and/or miniprotein design.
Strong communication skills and interest in bridging computational and experimental disciplines.

Responsibilities

Apply generative protein design workflows to produce and evaluate biologic-based binders against therapeutically relevant targets.
Build reproducible Python-based workflows for large-scale protein design, structural analysis, candidate ranking, and data visualization.
Apply physics-based modeling tools (e.g., Rosetta, MOE, Schrödinger) to assess binding energetics and developability properties.
Explore protein language model representations and lightweight ML models that consider protein sequence, structure, and physics-based features to predict and prioritize candidate properties.
Advance top designs to recombinant expression and experimental characterization (e.g. titer, BLI, DSF).
Document workflows, results, and recommendations clearly for use in future protein-engineering campaigns.
Showcase your work with a final presentation (PPT) near the conclusion of your internship