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

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.

• 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

Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

• 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

• Experience with genomic sequence models (Enformer, Borzoi, AlphaGenome, ChromBPNet, CREsted)
• Experience with experiment tracking platforms such as Weights & Biases or MLflow
• Experience with cloud platforms and with containers (Docker, Singularity/Apptainer) and workflow managers (Nextflow, Snakemake)
• Experience with active learning, Bayesian optimization, or reinforcement learning approaches to model fine-tuning
• Experience with the use of AI-assisted coding tools in a development workflow
• Experience releasing and maintaining open-source scientific software, including documentation, versioned releases, community support, and model-serving or API layers for outside users
• Familiarity with gene regulation, chromatin accessibility data, or single-cell genomics
• Experience contributing to large, collaborative consortium projects and shared open data resources
• Strong written and verbal communication skills, including the ability to explain technical tradeoffs to experimental biologists

• Working at a desk and at a computer developing software and analyzing data

• Fine motor movements in fingers/hands to operate computers and other office equipment

• This role is currently able to work both remotely and onsite in a hybrid work environment. We are a Washington State employer, and the primary work location for all Allen Institute employees is 615 Westlake Ave N.; any remote work must be performed in Washington State.

• Occasional travel to collaborator sites, consortium meetings, and national or international conferences

• *Please note, this opportunity offers relocation assistance
• *Please note, this opportunity may may offer visa sponsorship

$87,450 – $108,250 *

* Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable.