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Scientist - ML​/AI Regulatory Genomics and Cell Type-Targeted Tools Brain Health

Job in Seattle, King County, Washington, 98127, USA
Listing for: Allen Institute
Full Time position
Listed on 2026-10-11
Job specializations:
  • Research/Development
    Data Scientist
  • IT/Tech
    Data Scientist
Salary/Wage Range or Industry Benchmark: 87000 - 108000 USD Yearly USD 87000.00 108000.00 YEAR
Job Description & How to Apply Below
Position: Scientist I - ML/AI Regulatory Genomics and Cell Type-Targeted Tools for Brain Health

Scientist I – Regulatory genomics and cell type-targeted tools for brain health

The Allen Institute accelerates science for a healthier world through large-scale research designed to answer some of the most complex questions in biology. Our multi-disciplinary teams generate foundational knowledge, tools, and data to understand how our brain, cells, and immune system work. We share our work openly so others can build on it, move faster, and ask bigger questions. We drive discovery forward and create new possibilities for improving human health.

Brain Health is a new global collaborative research initiative designed to accelerate understanding of human brain diseases through large-scale human tissue analysis, open science, AI-enabled disease modeling, and translational platform technologies. Building on foundational advances in human brain cell atlases, quantitative neuropathology, single-cell and spatial biology, multimodal molecular profiling, and AI-enabled analysis, Brain Health is developing a scalable framework for understanding disease progression directly in the human brain and identifying new opportunities for therapeutic development.

This effort centers on a lab-in-the-loop AI platform that designs and validates synthetic enhancer sequences for precision cell-type targeting across the brain. We train sequence-to-function models on the Allen Institute’s cross-species whole-brain multiomic atlases, generate synthetic enhancers optimized for strength and specificity, and validate candidates in vivo with single-cell resolution across thousands of brain cell types. Thousands of enhancer AAVs have been screened in mice for functional activity using a consistent pipeline and released through the Genetic Tools Atlas .

Each round of screening adds new tools and feeds back into model training, tightening the loop between computational prediction and biological discovery. Models, validated sequences, and the underlying codebase will be openly released as a platform for designing synthetic regulatory elements in any tissue or cell type.

We are seeking a Scientist I to contribute to cross-team efforts defining the regulatory biology of this platform to interpret why designed sequences succeed or fail in the intact brain and to turn each round of in-vivo validation into a tuned model. In this role you will transform cross-species single-cell multiomic atlases into training and evaluation sets, analyze whole-brain enhancer-AAV screens across thousands of cell types, and work alongside scientists and machine learning colleagues to set the design objectives and prospective success criteria for each lab-in-the-loop cycle.

The ideal candidate is a regulatory genomics scientist who is fluent enough in modern sequence models to shape them, and who is motivated by the prospect of building genetic tools that reach disease-relevant cell types.

At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly.

We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

Essential Functions

  • Curate training, validation, and benchmark sets from cross-species single-nucleus multiomic data
  • Define cell-type targets and design objectives for synthetic enhancers from whole-brain single-cell atlas data
  • Apply sequence-to-function models and guided optimization to prioritize candidate enhancers, and…
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