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

• 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

Preferred Education and Experience

• 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

Work Environment

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

Physical Demands

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

Position Type/Expected Hours of Work

• 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.

Employees (and their families) are eligible to enroll in benefits per eligibility rules outlined in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institutes Benefits Guide. Details on the Allen Institute’s benefits offering are located at the following link to the Benefits Guide: https://alleninstitute.org/careers/benefits.

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.

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.

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.