Regulatory Genomics Scientist: Brain Health Tools (Hybrid)

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

Benefits offered by this job

Relocation assistance

Visa sponsorship may be available

Benefits package

Job summary

The Allen Institute is seeking a Scientist I to advance regulatory genomics and design cell-type targeted brain tools within a collaborative, cross-team environment. You will transform regulatory data from cross-species atlases into training sets and help shape design objectives for in‑vivo validation.

Ideal candidates have a Ph.D. in a related field and strong Python or R skills, with experience in sequence‑to‑function modeling and enhancer screens.

Qualifications

• Ph.D. in genomics, computational biology, neuroscience, genetics, or related field
• Experience with single‑nucleus multiomic, spatial, methylation, or 3D genome data
• Proficiency in Python or R for large datasets, with familiarity with sequence‑based deep learning and AI tools
• Experience with enhancer screens like MPRA, STARR‑seq, or enhancer‑AAV assays

Responsibilities

• Curate training and benchmark sets from cross‑species data
• Define cell‑type targets and design objectives for synthetic enhancers
• Apply sequence‑to‑function models and interpret results via motif content and spacing
• Plan design‑and‑test cycles with experimental colleagues and feed results back into models
• Publish findings and release validated sequences and benchmarks

Skills

Python

R

sequence models

Education

Ph.D. in genomics, computational biology, neuroscience, genetics, or related field

Tools

Enformer

Borzoi

ChromBPNet

Job description

The Allen Institute is seeking a Scientist I to advance regulatory genomics and design cell-type targeted brain tools within a collaborative, cross-team environment. You will transform regulatory data from cross-species atlases into training sets and help shape design objectives for in‑vivo validation.

Ideal candidates have a Ph.D. in a related field and strong Python or R skills, with experience in sequence‑to‑function modeling and enhancer screens.