Chime is seeking a Bioinformatics Machine Learning Intern to contribute to projects in single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development. In this role, you will apply machine learning to single-cell omics data to extract meaningful biological insights.
What You'll Do
- Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
- Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
- Explore and 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 following reproducible research best practices
- Present findings through technical reports, visualizations, and presentations to cross-functional teams
What We're Looking For
- Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
- Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
- Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
- Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
- Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
- Strong problem-solving skills and ability to communicate complex insights effectively
Nice to Have
- Machine Learning & AI: Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
- Machine Learning & AI: Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
- Machine Learning & AI: Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
- Machine Learning & AI: Exposure to representation learning, variational autoencoders, or contrastive learning methods
- Machine Learning & AI: Familiarity with scikit-learn, XGBoost, or similar ML libraries
- Machine Learning & AI: Interest in or experience with LLMs, RAG systems, or agentic AI tooling
- Bioinformatics: Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
- Bioinformatics: Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
- Bioinformatics: Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)
- Bioinformatics: Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)
- Engineering & Infrastructure: Familiarity with Linux/Unix CLI and version control (Git/GitHub)
- Engineering & Infrastructure: Experience with containerization (Docker, Singularity) and environment management (conda, venv)
- Engineering & Infrastructure: Exposure to cloud computing platforms (GCP preferred)
- Engineering & Infrastructure: Familiarity with workflow managers (Nextflow, Snakemake)
- Engineering & Infrastructure: Adherence to best-practices for conduct reproducible computational research
Technical Stack
- Python, R, Scanpy/scverse, Seurat, Bioconductor, PyTorch, TensorFlow, JAX, scikit-learn, XGBoost, scvi-tools/MultiVI/totalVI, Muon, Squidpy, cell2location, nf-core/spatialvi, CellChat, NicheNet, LIANA, Linux/Unix CLI, Git/GitHub, Docker, Singularity, conda, venv, GCP, Nextflow, Snakemake
Team & Environment
You will be part of the Bioinformatics team, collaborating with scientists, clinicians, and data scientists.
Benefits & Compensation
- Compensation: $34-$38 per hour
Work Mode
This role operates on a hybrid schedule and is open to candidates located in the United States.
Chime is an equal opportunity employer.
