Job description

Who We Are: Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico’s highly innovative technology labs, its commitment to curiosity-driven discovery science and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs. Position 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). Additional research can be found here . 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 Position Requirements: 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 The estimated base salary range for this role is $170,000 - $240,000. Actual pay will be based on a number of factors including experience and qualifications. This position is also eligible for two annual cash bonuses

Requirements

Must-haves

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).
Additional research can be found here .
Design and train deep learning models for biological sequence analysis, with emphasis on gene regulation, single-cell genomics, and variant interpretation
Communicate research through publications, open-source software, and public-facing tools
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaccessibilityaillm

Platform: greenhouse
Effort: quick