Vertex Fall Co-op 2026, Computational Genomics

Vertex Inc.•San Diego, CA

4d•$30 - $50•Onsite

About The Position

Kickstart Your Career at Vertex! Are you ready to make a real impact? At Vertex, our mission is to tackle serious diseases and to change lives, for the better, for the future. Our aim is to give you the skills, insights, and career guidance to be an important part of that future; to turn your potential into progression. As a Vertex intern or co-op, you’ll work on meaningful projects, collaborate with talented teams, and learn from industry leaders. We’re passionate about innovation, inclusion, and supporting your growth—inside and outside the lab. Why Vertex? Real Projects: You’ll work on assignments that make a real impact, not just busy work. Mentorship & Networking: Connect with leaders and peers who want to see you succeed through professional networks, connections, and collaborations that will shape your longer-term career. Flexible & Supportive: We offer flexible work options with Flex @ Vertex and prioritize your wellbeing. Inclusive Culture: Collaboration and inclusion are embedded in everything we do. Career Launchpad: Build skills, explore career paths, and get guidance for your future career. Ready to apply? Submit your application and let’s turn possibilities into reality! Your Impact The Vertex Computational Genomics co-op program is a 6-month experiential training program for students currently working towards an advanced or Doctoral degree. If you are passionate, collaborative, and growth-minded, a co-op at Vertex will help you gain meaningful experience in our Data and Computational Sciences functional areas and serve as a launchpad for your career. Please note that Vertex reviews applications on a rolling basis and reserves the right to close this job posting prior to the listed deadline. Applicants can expect to receive an update about their application before or shortly after the application deadline. Important Notice Regarding Internship and Co-op Inquiries At Vertex Pharmaceuticals, we are committed to providing a fair and structured recruitment process for all students interested in internship and co-op opportunities. To ensure consistency and equity, all student applications must go through our Early Talent Acquisition Team. Due to the high volume of interest, we are unable to respond to individual solicitation. Direct solicitation to Vertex employees- including senior leaders via email will result in removal from the recruiting process. We appreciate your enthusiasm and interest in Vertex. To be considered for internship or co-op roles, please apply directly through our official application channels. (https://www.vrtx.com/careers/career-growth-and-opportunities/co-ops/) Thank you for respecting our process and helping us maintain a fair experience for all candidates. What you will be doing: We are seeking a highly motivated, graduate-level co-op student to join Vertex’s Computational Genomics team in San Diego to support the evaluation and application of single-cell foundation models for transcriptomics analysis across disease areas. This role will provide hands-on experience at the intersection of computational genomics, AI/ML, and drug discovery. The co-op will work on assessing publicly available single-cell (sc) foundation models and their utility for scRNA‑seq data analysis, including tasks such as cell type annotation and workflow automation. The candidate will develop and execute benchmarking analyses, compare model outputs with existing computational approaches and reference annotations, and communicate findings to cross-functional partners. This role is intended for a graduate-level student with a strong interest in single-cell genomics and the application of AI/ML methods to scRNA-seq data to derive biological insights that can support downstream decisions.

Requirements

Enrolled in a graduate program, pursuing a Master’s, or Ph.D. in Computational Biology, Bioinformatics, Genetics, Genomics, Computer Science, Statistics, or related quantitative/biological field.
Familiarity with single-cell genomics data analysis and associated packages or frameworks (eg, Seurat or scanpy).
Experience with Python and/or R for scientific data analysis.
Experience working in AWS, Linux, or HPC/cluster environments for large-scale data analysis.
Strong analytical skills and demonstrated ability to plan, execute, and interpret computational analyses.
Strong written and verbal scientific communication skills.
Ability to work both independently and collaboratively in a team environment and to proactively seek guidance when needed.
Capacity to work onsite full-time in San Diego, CA
Legal authorization to work in the United States, now and in the future.
You must be available to work full-time, 40 hours per week from July – December 2026

Nice To Haves

Strong understanding of machine learning or statistical modeling for genomics applications.
Experience with benchmarking, evaluation metrics, and model comparison for high-dimensional biological data.
Familiarity with reproducible research practices such as version control, notebooks, documentation, and well-structured code organization.

Responsibilities

Evaluate publicly available single-cell foundation models for tasks relevant to Vertex genomics research, including cell type annotation and potential extension to in-silico perturbation and workflow automation.
Build and execute a computational cell type annotation framework to compare single-cell foundation model performance against manual labels and existing computational methods.
Perform data preprocessing, quality control, harmonization, annotation analysis, and downstream evaluation using Python- and/or R-based bioinformatics workflows.
Work in cloud or compute environments, including AWS and/or HPC infrastructure, to support scalable analysis of large genomics datasets.
Generate clear visualizations, summaries, and recommendations to communicate model performance, limitations, and opportunities for adoption to computational and biology stakeholders.
Collaborate with Computational Genomics scientists and cross-functional partners to interpret results and, if successful, help inform integration into existing analysis workflows.
Regularly share updates in both formal and informal meetings with the computational genomics team and AI working group, while keeping analysis documentation well-organized and reproducible.