Intern, Bioinformatics at Revolution Medicines in Redwood City, CA

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers.  The company’s R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding Revolutionaries in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

The Opportunity:

We are seeking a highly motivated Bioinformatics Intern to support integrative analysis of single-cell datasets aimed at understanding tumor microenvironment responses to KRAS inhibitors. This role will focus on harmonizing and analyzing multi-source single-cell sequencing data to characterize treatment-induced cellular and molecular changes within tumors.

The intern will work closely with computational biologists and translational scientists to generate insights that inform therapeutic strategy and combination approaches.

• Integrate multiple single-cell RNA-seq (scRNA-seq) across studies and platforms.

• Perform data preprocessing, quality control, normalization, and batch correction.

• Harmonize metadata and cell annotations across datasets.

• Conduct clustering, cell-type identification, and differential expression analyses.

• Characterize tumor microenvironment changes following KRAS inhibitor treatment, including immune infiltration shifts, myeloid reprogramming, T cell activation/exhaustion states, and stromal remodeling.

• Perform pathway enrichment and gene signature analyses.

• Generate clear visualizations.

• Develop reproducible, well-documented computational workflows.

• Present findings to cross-functional research teams.

Required Skills, Experience and Education:

• Proficiency in Python (preferred) or R for data analysis.

• Experience working with high-dimensional biological datasets.

• Familiarity with machine learning or statistical modeling.

• Strong data wrangling and preprocessing skills.

• Understanding of molecular biology and genomics concepts.

• Ability to write clean, reproducible, and well-documented code.

• Strong analytical thinking and problem-solving skills.

• Effective written and verbal communication skills.

Preferred Skills:

• Experience with single-cell analysis frameworks (e.g., Scanpy, Seurat, AnnData).

• Familiarity with batch correction and dataset integration methods (e.g., Harmony, scVI, Seurat integration).

• Experience analyzing RNA-seq, ATAC-seq, or other sequencing-based assays.

• Knowledge of tumor immunology or tumor microenvironment biology.

• Experience with pathway enrichment or gene set analysis tools.

• Prior experience in cancer genomics or translational research.

• Experience working in a collaborative research environment. 

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