Graduate Intern - R&D AI for Target Safety

Career CategoryCollege JobJob DescriptionJoin Amgen’s Mission of Serving PatientsAt Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.Undergraduate Intern – R&D AI & Translational Safety Data ScienceWhat You Will DoLet’s do this. Let’s change the world. During this program, you will help design and integrate AI tools that accelerate safety assessments from late discovery through post-marketing. You’ll build practical GPT-powered workflows that ingest literature, -omics, and internal datasets to surface mechanistic insights, identify potential liabilities, and support internal documentation—working closely with toxicologists, clinical safety scientists, computational biologists, and data engineering partners.Additional considerations for this role: emphasis on data governance, clear documentation for reproducibility and version control, interpretability of results, and thoughtful deployment within secure Amgen environments.Develop and refine GPT/LLM tools for data aggregation, processing, interpretation, and automated report generation across safety workflows.Build literature-mining and evidence-triage pipelines (e.g., extracting adverse findings, targets, pathways, phenotypes) to inform target safety assessments.Integrate multimodal data (public and internal)—including omics, preclinical findings, clinical safety signals, and real-world evidence—into coherent risk narratives.Generate mechanistic off-target hypotheses and pathway-level safety rationales to support internal safety evaluation and documentation.Apply Responsible/Trustworthy AI practices: bias testing, interpretability, human-in-the-loop review, secure handling of sensitive data.Write clear documentation (methods, assumptions, limitations) and contribute to automation codebase and templates for Target Liability.Present progress and findings to TSRS stakeholders; incorporate feedback to iterate on tools and analyses.Collaborate with TSRS team members to prioritize use cases with maximum impact.What We Expect of YouWe are all different, yet we all use our unique contributions to serve patients. The individual we seek must demonstrate a willingness to embrace new challenges, showing enthusiasm for learning and exploring unfamiliar topics and have these qualifications:Basic Qualifications:Amgen requires that all individuals applying for an undergrad internship or a co-op assignment at Amgen must meet the following criteria:18 years or olderCurrently enrolled in a full-time Bachelor’s Degree program from an accredited college or university with a 3.0 minimum GPA or equivalentCompletion of one year of study from an accredited college or university prior to the internship commencingEnrolled in a full-time Bachelor’s degree program following the potential internship with an accredited college or universityMust not be employed at the time the internship startsStudent must be located in the United States for the duration of the internshipPreferred QualificationsCurrently enrolled in an undergraduate program in Computational Biology, Bioinformatics, Computer/Data Science, Toxicology, Life Sciences, or a related field.Foundational coursework in computer science, statistics/probability and data analysis, AI fundamentals, biology, or toxicology.Proficiency in Python and R for data analysis and modeling (pandas/NumPy/scikit-learn; tidyverse/Bioconductor).Experience with Jupyter/R Markdown, SQL, Git/GitHub, and environment management (conda/renv).Coursework or hands-on projects in AI/ML; familiarity with NLP/LLMs (prompt engineering, retrieval-augmented generation, summarization).Experience working with -omics datasets and biological knowledge bases/ontologies (e.g., GO, MedDRA, etc.), and public biology resources such as Open Targets or similar.Comfort integrating heterogeneous data (e.g. preclinical, clinical, literature) and mapping to pathways/phenotypes.Familiarity with target liability assessment, off-target mechanisms, and translational safety principles.Familiarity with APIs or simple workflow orchestration; basic cloud literacy (AWS/Azure/GCP) a plus.Experience building dashboards