Bioinformatics Software Engineer

Company Description

By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.

Why join Harvard Medical School?

Harvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.

You’ll be at the heart of biomedical discovery, education, and innovation, working alongside world-renowned faculty and a community dedicated to improving human health. This is more than a job - it’s an opportunity to shape the future of medicine.

Job Description

Job Summary:

Participate in the design of software that supports and enriches research productivity and reliability; implement software solutions.  Develop software and data services with researchers to ensure that modern standards of reproducible code are kept.

Job-Specific Responsibilities:

We are looking for a highly skilled Bioinformatics Software Engineer who specializes in designing, developing, deploying, and maintaining scalable bioinformatics pipelines on cloud-based infrastructure. The candidate will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT Data Analysis Center that manages multi-omic data (e.g., Illumina/PacBio/ONT Whole Genome Sequencing (WGS), RNA-Seq). The ideal candidate will have a deep understanding of next-generation sequencing (NGS) data analysis, workflow automation, cloud computing, and cloud software engineering best practices. This role will support research and production environments where reproducibility, scalability, and performance are critical.

Job Description: 

• Design, implement, and maintain bioinformatics pipelines for high-throughput sequencing data (e.g., alignment, QC, variant calling from WGS and RNA-seq) similar to those in existing repositories: https://github.com/smaht-dac/main-pipelines.
• Build reproducible, well-tested, and automated workflows using workflow management systems (particularly CWL).
• Architect and manage AWS-based compute infrastructure to support pipeline execution, including automated deployment, scaling, and monitoring.
• Containerize workflows using Docker or similar tools for managed execution and portability.