Machine Learning Scientist / Senior Machine Learning Scientist

<h4><strong>Who We Are:</strong></h4> <p>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.</p> <p><strong>Position Description</strong>:</p> <p>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.</p> <p>This work builds on a sustained research program at the intersection of deep learning and regulatory genomics, including:</p> <ul> <li><a href="https://www.nature.com/articles/s41592-021-01252-x">Avsec, Ž. <em>et al.</em> Effective gene expression prediction from sequence by integrating long-range interactions. <em>Nat Methods</em> <strong>18</strong>, 1196–1203 (2021).</a></li> <li><a href="https://www.nature.com/articles/s41592-022-01562-8">Yuan, H. &amp; Kelley, D. R. scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. <em>Nat Methods</em> <strong>19</strong>, 1088–1096 (2022).</a></li> <li><a href="https://www.nature.com/articles/s41588-024-02053-6">Linder, J., Srivastava, D., Yuan, H., Agarwal, V. &amp; Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics (2025).</a></li> </ul> <p>Additional research can be found <a href="https://scholar.google.com/citations?hl=en&amp;user=NYzqnv0AAAAJ&amp;view_op=list_works&amp;sortby=pubdate">here</a>.</p> <p><strong>Position Responsibilities:</strong></p> <ul> <li>Design and train deep learning models for biological sequence analysis, with emphasis on gene regulation, single-cell genomics, and variant interpretation</li> <li>Partner with experimental scientists to connect model predictions to biological mechanisms — designing validation experiments, analyzing large-scale genomics