Bioinformatics Machine Learning Intern (AI)

TL;DR

Bioinformatics Machine Learning Intern (AI): Applying machine learning and generative AI methods to single-cell omics data to discover pathways to life beyond disease with an accent on multiomics integration and precision medicine. Focus on developing deep learning models for cell type classification, biomarker discovery, and accelerating biological data interpretation.

Location: Remote or Hybrid (United States)

Salary: $34-$38 per hour

Company

hirify.global advances care by integrating top-tier clinical and biological data to discover pathways to life beyond disease.

What you will do

• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development.
• Apply and evaluate machine learning and deep learning models for cell type classification and patient stratification.
• Prototype generative AI and LLM-based approaches to accelerate scientific workflows and biological data interpretation.
• Collaborate with scientists and clinicians to design and execute data-driven research projects.
• Optimize and document computational workflows following reproducible research best practices.
• Present technical findings and visualizations to cross-functional teams.

Requirements

• Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related field.
• Experience processing and analyzing single-cell data (scRNA-seq, scATAC-seq, etc.) using Scanpy, Seurat, or Bioconductor.
• Applied expertise in developing ML/deep learning models (GNNs, transformers, autoencoders) for biological data.
• Proficiency in Python and/or R for data analysis and statistical modeling.
• Strong understanding of statistical methods for biological data, including hypothesis testing and clustering.
• Must be based in the United States.

Nice to have

• Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Familiarity with ML experiment tracking tools like MLflow or Weights & Biases.
• Experience with multimodal single-cell integration and spatial transcriptomics analysis.
• Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets).
• Proficiency with Linux CLI, Git, Docker, and workflow managers like Nextflow or Snakemake.

Culture & Benefits

• Mission-driven environment focusing on maximizing patient impact.
• Opportunity to work with high-quality clinical and biological data.
• Company values: Act with Purpose, Be Curious, Take Ownership, Invest in Relationships, and Embrace Agility.