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Bioinformatics Machine Learning Intern

3 weeks ago
United States
Internship
Entry Level
Machine Learning Engineer
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 for tasks such as cell type classification, biomarker discovery, and patient stratification.
  • Prototype generative AI and LLM-based approaches to accelerate 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.

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 using single-cell analysis frameworks such as Scanpy/scverse, Seurat, or Bioconductor.
  • Applied experience developing and evaluating machine learning or deep learning models on biological data.
  • Experience with neural network architectures such as GNNs, transformers, or autoencoders, including model selection and benchmarking.
  • Proficiency in 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 (preferred).
  • Familiarity with graph neural networks, attention mechanisms, transformer architectures, ML experiment tracking, representation learning, variational autoencoders, contrastive learning, scikit-learn, XGBoost, or LLM/RAG/agentic AI tooling (preferred).
  • Experience with multimodal single-cell integration tools such as Seurat WNN, scvi-tools/MultiVI/totalVI, or Muon (preferred).
  • Familiarity with spatial transcriptomics analysis tools such as Squidpy, cell2location, or nf-core/spatialvi (preferred).
  • Experience with cell-cell communication inference tools such as CellChat, NicheNet, or LIANA (preferred).
  • Knowledge of drug-gene interaction resources such as CMap/LINCS, OpenTargets, or ChEMBL (preferred).
  • Familiarity with Linux/Unix CLI, Git/GitHub, Docker or Singularity, conda or venv, cloud platforms such as GCP, and workflow managers such as Nextflow or Snakemake (preferred).
  • Commitment to reproducible computational research best practices.
  • Ability to commit to an 8–10 week internship.

Benefits

  • Compensation of $34–$38 per hour.
  • Hybrid or remote work in the United States.
  • 8–10 week internship duration.
  • Opportunity to work on active projects in single-cell biology, multiomics integration, precision medicine, and drug development.

Interested in this position?

Apply directly on the company website

Apply Now

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