Scientist I - ML/AI Foundational Models for Synthetic Enhancer Design

We seek a Scientist I to build the computational backbone of a lab-in-the-loop platform that designs and validates synthetic enhancers for precision cell-type targeting in the brain. In this cycle, model-designed sequences are tested experimentally, and the results retrain the next round of models. You will contribute to an open-source codebase for training, fine-tuning, evaluating, and running inference on genomic sequence-to-function models. You will scale training across our cloud GPU infrastructure, turn large-scale cross-species whole-brain multi-omic data into reproducible training sets, and build on the thousands of enhancer-AAV vectors already screened and released publicly through the Allen Institute Genetic Tools Atlas to inform the design of novel synthetic enhancers.

The ideal candidate is a strong scientific software engineer and ML practitioner excited to share their models, sequences, and code as community tools for exploring regulatory genomics and designing enhancers in any tissue.

Ph.D. in computer science, computational biology, bioinformatics, applied mathematics, engineering, or a related field; or an equivalent combination of degree and experience

Strong software engineering practice in Python, including version control, testing, code review, dependency management, and 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

Familiarity with modern foundation models, including transformer architectures and large language models.

Proven experience working independently and in a collaborative, fast-paced team environment
Primary work location is Seattle; any remote work must be performed in Washington State.