2026 Summer Intern - Early Clinical Development - AI Modeling

Genentech • Daly City, CA

3d • $50 - $50 • Onsite

About The Position

A healthier future. It is what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That is what makes us Roche.
Within the Clinical Insight and Automation (CI&A) team of the Early Clinical Development (ECD) department at Roche/Genentech, we develop quantitative and AI-driven methods that accelerate study design, evidence generation, and decision-making. This internship will contribute to an applied research effort on generative modeling and causal inference for creating high-fidelity synthetic clinical data, with an emphasis on producing a reproducible outcome by leveraging conditional deep generative models and open-source foundation models; as well as an use case for an AI-Driven Root Cause Analysis for Clinical Data Queries solution.
This internship position is located in South San Francisco, on-site.

Requirements

• Must be pursuing a PhD (senior enrolled student).
• Required Majors: Computer Sciences, Artificial Intelligence, Computational Sciences, or a related field with a focus on machine learning systems or similar.
• Programming proficiency in Python; hands-on experience with PyTorch and scientific computing libraries (NumPy, Pandas).
• Experience developing and training deep learning models, with familiarity in modern generative modeling (e.g., diffusion models, VAEs, autoregressive/transformer models).
• Strong understanding of statistical machine learning and experimental methodology (ablations, error analysis, and appropriate statistical evaluation).
• Foundational understanding of causal inference and counterfactual reasoning (e.g., confounding, estimands, treatment-effect estimation) and how these considerations interact with modeling choices.
• Demonstrated technical writing skills (e.g., research reports or papers); comfort preparing conference-style manuscripts in LaTeX and presenting results to technical audiences.
• Commitment to reproducible research and software engineering best practices (version control, documentation, experiment tracking), with the ability to package artifacts for peer review; collaborative communication skills.

Nice To Haves

• Excellent communication, collaboration, and interpersonal skills.
• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
• Prior publication or strong experience preparing submissions for top-tier ML venues (e.g., NeurIPS/ICML/ICLR), including familiarity with reproducibility expectations (paper checklist, artifact preparation).
• Experience working with healthcare/biomedical datasets (EHR, claims, or clinical trial data); familiarity with data standards (OMOP, FHIR) is a plus.
• Knowledge of synthetic data evaluation and privacy risk assessment (e.g., memorization tests, membership inference, differential privacy).
• Familiarity with causal ML topics such as causal representation learning, domain adaptation, and externally controlled trial methodology.
• Experience with scalable training environments (GPUs, distributed computing) and modern ML tooling (Docker, experiment tracking platforms).
• Experience leveraging foundation models (LLMs) or structured prompting to incorporate domain knowledge into ML workflows is beneficial, but not required.
• Ability to query and extract data from relational databases using SQL and hands-on experience with NLP frameworks and clustering algorithms.
• Familiarity with clinical trial operations, Electronic Data Capture (EDC) systems, or the regulatory landscape of the pharmaceutical industry is beneficial, but not required.

Responsibilities

• Identify and prepare appropriate clinical datasets, define generation targets, and implement reproducible preprocessing pipelines.
• Develop and compare modern generative modeling approaches for patient-level outcomes and trajectories (e.g., diffusion, transformer, and latent-variable models) conditioned on baseline covariates and study design assumptions; and unsupervised clustering/topic models to identify clinically meaningful patterns.
• Incorporate causal inference considerations (confounding control, covariate balance, estimands) and quantify how synthetic controls impact downstream treatment-effect estimation; and perform manual "gold standard" labeling to create high-quality training datasets.
• Design rigorous evaluation protocols for fidelity and utility, including distributional similarity, calibration/uncertainty, fairness and subgroup robustness, and privacy-risk checks, with ablations and sensitivity analyses.
• Build end-to-end experiment infrastructure (training and evaluation scripts, configuration management, and experiment tracking) to support reproducibility and efficient iteration.
• Co-prepare a conference-quality manuscript, figures, and supplementary materials, including the paper checklist and (where appropriate) anonymized code/data artifacts consistent with reproducibility and ethics expectations.
• Communicate progress through regular updates; deliver a final technical report, curated repository, and presentation to cross-functional stakeholders.
• Develop and execute Python scripts to ingest, clean, and normalize large volumes of unstructured clinical query text and patient-level datasets.
• Help translate technical AI findings into a "Recommendations Matrix" that suggests specific site training or system improvements for stakeholders.

Benefits

• Intensive 12-weeks full-time (40 hours per week) paid internship.
• Program start dates are either in May/June, 2026.
• A stipend, based on location, will be provided to help alleviate costs associated with the internship.
• Ownership of challenging and impactful business-critical projects.
• Work with some of the most talented people in the biotechnology industry.
• paid holiday time off benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree