Calico
Machine Learning Scientist / Senior Machine Learning Scientist
South San Francisco, CA · Senior
Sponsorship not specified$170k-$240kDetected 16 days ago
Data StructuresAlgorithmsMachine LearningDeep LearningLLMsStatisticsAccessibilityBioinformaticsMolecular BiologyResearch
About the role
- We use these models to interpret human genetic variation, map causal regulatory mechanisms, and identify promising intervention points.
- Effective gene expression prediction from sequence by integrating long-range interactions.
- Nat Methods 18, 1196-1203 (2021). - Yuan, H. & Kelley, D.
Responsibilities
- 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
Requirements
- Substantive knowledge of molecular biology and genetics
- familiarity with genomic data types and public data resources
Compensation
- 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
Benefits
- Design and train deep learning models for biological sequence analysis, with emphasis on gene regulation, single-cell genomics, and variant interpretation
- Deep expertise in machine learning with solid grounding in algorithms, data structures, and statistics
- Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation.
Company info
- 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:
- 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.
- 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.
- 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.
This listing is sourced directly from Calico's careers page and normalized into a canonical job model.