Intern - GenAI

We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society.

Genentech’s Data, Digital, and Analytics (DDA) organization applies data science and AI to high-impact business needs across Commercial, Medical, and Government Affairs (CMG). We collaborate with business stakeholders and technology partners to translate advances in generative AI into practical applications that drive measurable business impact.

As an Applied GenAI Scientist Intern, you will explore how intelligent agents can reason, use specialized tools, and execute multi-step business workflows across CMG. Your project will focus on developing and evaluating a simple, reusable approach for adapting advanced GenAI agents to business use cases. Building on the Deep Claude Agent approach, which uses Claude Code as an agent runtime, you will investigate how task instructions, reusable skills, context, and tool access affect agent performance.

You will develop a working prototype and apply it to a focused CMG use case selected by your manager. Working with data scientists, business stakeholders, and platform partners, you will generate evidence and practical guidance for extending the approach to additional workflows.

• Define the use case and evaluation approach.
• Translate a selected CMG business need into a focused agent task, research questions, and measurable success criteria.
• Design and prototype specialized GenAI agents.
• Combine task instructions, reusable skills, and relevant tools and data to enable an agent to carry out the selected workflow.
• Assess and de-risk the model components the agent depends on.
• Compare hosted and open-weight options for specialized components such as the fast, calibrated classifiers used in routing, guardrails, and scoring, weighing accuracy, latency, and cost alongside licensing and data-handling terms.
• Recommend self-hostable alternatives where a dependency cannot be adopted as-is.
• Conduct systematic experiments.
• Investigate how agent configuration, context, memory, and tool selection influence reasoning, output quality, and task completion.
• Analyze failure modes and identify where human review is needed.
• Evaluate business value and reliability.
• Compare the agent’s performance with an agreed baseline, assessing accuracy, consistency, time savings, and execution cost.
• Develop a reusable capability and share findings.
• Deliver a working prototype, adaptable agent configurations, and clear guidance that makes the approach easier to apply across CMG use cases.
• Present results, limitations, and recommendations to stakeholders.
• Bring the latest techniques from academia into our organization and apply to our use cases.

• Intensive 6-months, full-time (40 hours per week), paid internship.
• Program start dates are in January 2027.
• A stipend, based on location, will be provided to help alleviate costs associated with the internship.
• Ownership of an applied GenAI project, from experimental design through demonstration and evaluation.
• Collaboration with data scientists and business experts in the biotechnology industry.

• Must be pursuing a Master’s Degree (enrolled student).
• Must have attained a Master’s Degree.
• Must be pursuing a PhD (enrolled student).

Artificial Intelligence, Machine Learning, Computer Science, Data Science, Statistics, or a related quantitative discipline.

• Foundational understanding of machine learning and generative AI, including large…