Computational Biologist: Algorithms and Machine Learning in Therapeutic Discovery

Seeking scientists passionate about developing algorithms and deploying tools for the analysis of -omic data on our disease cancelling technology team.

Immuneering’s Disease Cancelling Technology (DCT) computational platform accelerates target identification and drug discovery.  We are expanding DCT by creating tools and interactive interfaces to facilitate interpretation of the insights generated by our algorithms.

We are looking for a computational biologist with a strong background in leveraging machine learning and statistical approaches to develop new analysis algorithms. The ideal candidate is an expert at applying statistical analysis and machine learning to genomic datasets, and has implemented new approaches in an R or python environment.

Our team consists of brilliant people willing to share their knowledge and eager to learn from each other. You will work in a collaborative and nurturing environment that values diversity, personal development and integrity.  We believe that diverse perspectives and experiences drive innovation. Immuneering is a Great Place to Work-Certified company with an excellent work-life balance.


San Diego, New York or Cambridge


  • Develop algorithms to leverage large genomic datasets using machine learning and statistical approaches
  • Create bioinformatic tools using custom R packages
  • Create and deploy interactive shiny apps for visualization of results from our Disease Cancelling Technology
  • Leverage SQL or other databases for data storage
  • Present scientific material (written and oral) to diverse audiences

Minimum Qualifications

  • PhD in Bioinformatics, Computational Biology, Biostatistics or a related field (i.e., Biology, Engineering, Computer Science, Mathematics, Statistics)
  • Broad and deep understanding of genetics, proteomics, and/or genomics as documented by a strong publication record in high-impact journals
  • Proficiency writing libraries to analyze large data sets (transcriptomic, genomic, proteomic, and/or epigenomic data)
  • Experience applying machine learning approaches to the study of human disease, ideally leveraging transcriptomic or functional genomics data
  • Proficient in R, comfortable writing functions and unit tests
  • Familiarity with the libraries tensorflow, keras or tidymodels
  • Ability to work independently as well as contribute to large projects
  • Willingness to learn
  • Effective English communication skills (both written and oral)

Preferred Additional Qualifications

  • Experience processing and interpreting single cell RNA-seq datasets
  • Experience developing effective visualization tools using ggplot2, plotly, d3
  • Basic experience developing and deploying shiny apps
  • Understanding of tidyverse packages
  • Knowledge of version control with git
  • Experience creating and managing docker images
  • Python scripting, especially numpy, pandas, scikit learn
  • Knowledge of clean code and test driven development
  • Experience interacting with AWS / cloud storage


To apply, contact ATTN: DCT Hiring Manager, or submit your resume here.