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# Machine Learning Scientist / Senior Machine Learning Scientist

- **Company:** Calico LLC
- **Location:** South San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $170,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Bioinformatics, Computational Biology, Data Structures, Machine Learning, Large Language Models, Deep Learning, Convolutional Neural Networks, Information Technology, Free and Open-Source Software
- **Published:** May 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=65f9c26042b211f6

## About the Role

Do you have experience in Tooling?, PhD in computational biology, bioinformatics, computer science, or a related field, and 0-5 years (for Scientist level) or 5+ years (for Senior Scientist level) of additional training in an industry or academic setting, with a strong publication record

 * Deep expertise in machine learning with solid grounding in algorithms, data structures, and statistics
 * Substantive knowledge of molecular biology and genetics; familiarity with genomic data types and public data resources
 * Hands-on experience analyzing genomics sequencing data, ideally including single-cell assays
 * Fluency with modern AI-assisted development and research tools (e.g., LLM-based coding assistants, literature synthesis), with a habit of proactively integrating new tools to accelerate scientific workflows
 * A collaborative disposition, strong follow-through, and comfort working at the interface of computation and experiment
 * Must be willing to work onsite at least four days per week

## Description

Calico is seeking a machine learning scientist to join a research group investigating how genome sequence determines regulatory function and how dysregulation of these programs drives aging. We develop sequence-based deep learning models that predict gene expression, chromatin accessibility, and other functional readouts directly from DNA. We use these models to interpret human genetic variation, map causal regulatory mechanisms, and identify promising intervention points.

This work builds on a sustained research program at the intersection of deep learning and regulatory genomics, including:

 * Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196-1203 (2021).
 * Yuan, H. & Kelley, D. R. scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nat Methods 19, 1088-1096 (2022).
 * Linder, J., Srivastava, D., Yuan, H., Agarwal, V. & Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics (2025).

Position Responsibilities:

 * Design and train deep learning models for biological sequence analysis, with emphasis on gene regulation, single-cell genomics, and variant interpretation
 * Partner with experimental scientists to connect model predictions to biological mechanisms - designing validation experiments, analyzing large-scale genomics data, and translating computational findings into actionable biological insights
 * Communicate research through publications, open-source software, and public-facing tools


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