Principal Scientist

Cytokinetics•South San Francisco, CA

1d•$202,500 - $236,250

About The Position

Cytokinetics is seeking a computational scientist to lead translational biomarker discovery at the intersection of human genetics, multi-omics, and AI/ML. The role focuses on identifying biomarkers that diagnose disease, predict disease progression, and anticipate patient outcomes in conditions involving impaired cardiac and skeletal muscle function. The scientist will combine genetic evidence, multi-omics profiles, and clinical phenotypes to determine which patients are at risk and investigate the underlying reasons. Working closely with experimental biologists and clinical research teams, the Principal Scientist will translate insights from patients into discovery and from discovery toward clinical application. This is a hands-on computational leadership position focused on applying modern AI/ML to human data in muscle disease. The successful candidate will lead cross-functional projects and mentor scientists. Applicants should submit a CV, a cover letter outlining their research experience and interests, and a list of publications. The expected salary range for the primary location of South San Francisco, California, is stated as $202,5000–$236,250; base pay may vary based on internal equity, geographic region, job-related knowledge, skills, and experience. Base salary is one component of total compensation, which includes equity in the form of stock options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401(k) retirement benefits with employer match, learning and wellness stipends, and paid time off. Benefits are subject to change and may vary by jurisdiction.

Requirements

Ph.D. in Computational Biology, Human Genetics, Bioinformatics, or a related field, with 6 or more years of postdoctoral or industry experience.
Strong expertise analyzing large-scale genomic datasets, including GWAS and sequencing data.
Deep expertise in transcriptomics, proteomics, or single-cell analysis, with integration across data types.
Track record translating human molecular or phenotypic data into disease mechanisms, therapeutic targets, or biomarker strategies.
Proficiency in R and/or Python.
Expertise in study design, batch effects, confounding, longitudinal data, and evaluating AI/ML methods.
Strong interest in applying AI tools, including LLMs and agents, to translational research; production experience is not required.
Ability to lead cross-functional projects and mentor scientists.

Nice To Haves

Experience with population biobanks such as UK Biobank or All of Us, or with disease-specific cohorts.
Drug discovery experience, including biomarker analysis, patient sample studies, or patient stratification.
Experience integrating genetic evidence with single-cell or tissue-specific expression data.
Previous therapeutic area experience in cardiovascular and neuromuscular disease is strongly desired.
Experience prototyping LLM-assisted or agentic workflows.

Responsibilities

Integrate genomic, transcriptomic, proteomic, and single-cell data to trace how genetic variation drives molecular and cellular changes in cardiac and skeletal muscle disease.
Discover and validate diagnostic and prognostic biomarkers of disease onset, progression, and clinical outcomes using omics and longitudinal clinical data from biobanks and patient cohorts.
Develop and validate AI/ML methods that link molecular profiles with imaging and clinical phenotypes to define patient subgroups for precision medicine.
Investigate disease heterogeneity and genotype-phenotype discordance to uncover modifier pathways and generate mechanistic hypotheses for experimental follow-up.
Lead translational applications of LLMs and agents, such as evidence synthesis and data curation, with appropriate benchmarking.
Lead cross-functional projects and mentor scientists.