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Bioinformatics Machine Learning Intern

16 hours, 1 minute ago
United States
Internship
Entry Level
Data Scientist
Data Science and Analytics
Agile Deep Learning Docker GCP Generative AI Git GitHub Linux LLM Machine Learning MLflow Python PyTorch R Scikit-learn TensorFlow Transformers Unix XGBoost
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RefinedScience

RefinedScience

At RefinedScience, we seamlessly integrate top-tier clinical and biological data with expert knowledge to provide unparalleled insights. We maximize patient impact with these unique insights by optimizing clinical trial probability of success and time to actionable results. We work across biopharma and we are a trusted partner in achieving better results, faster – working together to unlock strategic advantage.

research
11-50
Founded 2019
View All Jobs 2

Description

  • Analyze single-cell and multiomics datasets to generate biological insights for precision medicine and drug development programs.
  • Apply and evaluate machine learning and deep learning methods on single-cell data for classification, biomarker discovery, and patient stratification.
  • Prototype generative AI and LLM-based approaches to improve biological data interpretation and scientific workflows.
  • Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects.
  • Document and optimize computational workflows using reproducible research best practices.
  • Present findings through technical reports, visualizations, and presentations to cross-functional teams.
  • Support computational biology and large-scale data analysis efforts within the bioinformatics team.

Requirements

  • Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field.
  • Experience processing, analyzing, and interpreting single-cell omics data such as scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics.
  • Experience with frameworks such as Scanpy/scverse, Seurat, or Bioconductor.
  • Applied experience developing and evaluating machine learning and deep learning models on biological data.
  • Experience with neural network architectures such as GNNs, transformers, or autoencoders.
  • Ability to use Python and/or R for data analysis, statistical modeling, and visualization.
  • Understanding of statistical methods for biological data, including hypothesis testing, differential expression, multiple testing correction, and clustering.
  • Strong problem-solving skills and ability to communicate complex insights effectively.
  • Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX is preferred.
  • Familiarity with ML experiment tracking tools such as MLflow or Weights & Biases is preferred.
  • Interest in or experience with LLMs, RAG systems, or agentic AI tooling is preferred.
  • Experience with multimodal single-cell integration tools such as Seurat WNN, scvi-tools/MultiVI/totalVI, or Muon is preferred.
  • Familiarity with spatial transcriptomics analysis tools such as Squidpy, cell2location, or nf-core/spatialvi is preferred.
  • Experience with cell-cell communication inference tools such as CellChat, NicheNet, or LIANA is preferred.
  • Familiarity with Linux/Unix CLI and version control tools such as Git/GitHub.
  • Experience with containerization tools such as Docker or Singularity and environment management tools such as conda or venv.
  • Exposure to cloud computing platforms, with GCP preferred.
  • Familiarity with workflow managers such as Nextflow or Snakemake.
  • The internship duration is 8–10 weeks.

Benefits

  • Hybrid or remote work in the United States.
  • Compensation of $34–$38 per hour.
  • Opportunity to work on active projects in single-cell biology, multiomics integration, precision medicine, and drug development.
  • Exposure to cross-functional collaboration with scientists, clinicians, and data scientists.

Interested in this position?

Apply directly on the company website

Apply Now

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