Scientist I: Regulatory Genomics for Brain AI

alleninstitute; Seattle (WA); Hybrid; USD 87,000 - 108,000; Full time

Benefits offered by this job

Relocation assistance

Visa sponsorship

Job summary

The Allen Institute seeks a Scientist I to advance regulatory genomics within its Brain Health initiative. You will transform cross-species single-cell atlases into training cohorts, analyze whole-brain enhancer screens, and collaborate with machine-learning scientists to set design objectives for each lab-in-the-loop cycle.

Ideal candidates will have a regulatory genomics background, experience with sequence models, and a drive to build genetic tools reaching disease-relevant brain cell types.

Qualifications

• Ph.D. in genomics, computational biology, neuroscience, genetics, or a related field
• Experience with single-nucleus multiomic, spatial, methylation, or 3D genome data and enhancer screens (MPRA, STARR-seq, enhancer-AAV)
• Research experience in gene regulation or regulatory genomics with publications
• Proficiency in Python or R for large datasets; familiarity with sequence-based deep learning and AI tools

Responsibilities

• Curate training, validation, and benchmark sets from cross-species single-nucleus multiomic data
• Define cell-type targets and design objectives for synthetic enhancers from brain atlas data
• Apply sequence-to-function models and guided optimization to prioritize enhancers and interpret them via motif content and spacing
• Plan design-and-test cycles with experimental colleagues and feed results back into model training
• Publish findings and release validated sequences and benchmarks openly

Skills

Python/R proficiency

Seq-to-function models

Handling large datasets

Education

Ph.D. in genomics or related field

Equivalent experience

Tools

Enformer

Borzoi

ChromBPNet

Job description

The Allen Institute seeks a Scientist I to advance regulatory genomics within its Brain Health initiative. You will transform cross-species single-cell atlases into training cohorts, analyze whole-brain enhancer screens, and collaborate with machine-learning scientists to set design objectives for each lab-in-the-loop cycle.

Ideal candidates will have a regulatory genomics background, experience with sequence models, and a drive to build genetic tools reaching disease-relevant brain cell types.