ML Scientist: Foundational Models for Brain Enhancer Design

ClearCompany Talent Management Software; Seattle (WA); On-site; USD 110,000 - 150,000; Full time

Job summary

The Allen Institute in Seattle is seeking a Scientist I to build the computational backbone of a lab-in-the-loop platform that designs and validates synthetic enhancers for brain cell targeting. You will scale training across cloud GPUs, transform cross-species data into training sets, and contribute to open-source code for training and inference of genomic sequence-to-function models.

Strong ML and software engineering background is encouraged.

Qualifications

• Ph.D. in CS, computational biology, or related field, or an equivalent combination of degree and experience
• Strong software engineering in Python, including reproducible environments
• Experience training deep learning models in PyTorch, JAX, or TensorFlow on multi-GPU or distributed infrastructure
• Experience building data pipelines for large scientific datasets that do not fit in memory

Responsibilities

• Build and maintain an open-source codebase for training, fine-tuning, and serving genomic sequence models, and package models and benchmarks for use outside the Institute
• Train large-scale whole-brain models efficiently in multi-GPU cloud environments
• Turn cross-species multi-omic data into reproducible, versioned training sets
• Implement active-learning loops that feed in vivo screening results back into the next round of models
• Publish and present findings in peer-reviewed journals and at scientific conferences

Skills

Python

PyTorch

JAX

TensorFlow

Data pipelines

Education

PhD in CS/CB/related field

Tools

Git

Conda

Job description

The Allen Institute in Seattle is seeking a Scientist I to build the computational backbone of a lab-in-the-loop platform that designs and validates synthetic enhancers for brain cell targeting. You will scale training across cloud GPUs, transform cross-species data into training sets, and contribute to open-source code for training and inference of genomic sequence-to-function models.

Strong ML and software engineering background is encouraged.