Scientist III • AI Scientist for Quantitative Neuropathy and ML Scalable Infrastructure We are searching for a
Scientist III in Scalable AI/ML for Neuropathology who will develop, scale, and deploy state-of-the-art machine learning methods, along with the infrastructure that supports them, to integrate multimodal neuropathological data, model disease progression, and drive the mechanistic understanding of brain disease at cellular resolution. This role sits at the intersection of computational biology, neuropathology, and ML engineering, working within the Brain Health Accelerator and collaborating with experimental neuropathologists and software engineers. AI/ML focus areas could include multimodal foundation models, diffusion models, and computer vision architectures, together with the distributed training, data pipelines, and model serving needed to run them at scale.
Essential Functions - Develop and deploy large-scale ML models integrating multimodal neuropathological data, including digital histology, spatial transcriptomics, MRI, and clinical metadata, to characterize brain disease progression at cellular resolution
- Design and apply computer vision architectures (ViT and related models) and foundation models to quantitative neuropathology image analysis
- Design, build, and maintain scalable data pipelines that render diverse biological and neuropathological datasets, including gigapixel whole-slide images and spatial omics, ML-ready across cloud and on-premises GPU infrastructure
- Implement and optimize distributed training and inference workflows for foundation models, including multi-GPU and multi-node training, experiment tracking, and reproducible model versioning
- Apply Bayesian statistical and causal modeling approaches to describe and predict neurodegenerative disease trajectories
- Collaborate with scientists, computational biologists, neuropathologists, and software engineers within the Allen Institute and with external partners to advance model development from research to scientific impact
- Promote open science through publication of peer-reviewed papers and release of open-source code, models, and reusable ML tooling
*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.
Required Education and Experience - Ph.D. in Computer Science, Applied Mathematics, Computational Biology, Computational Neuroscience, Statistics, or related field; or equivalent combination of degree and experience
- Minimum of 5 years of postdoctoral or industry research experience
- Demonstrated ability to design, implement, and apply deep learning models to large-scale biological or medical image data
- Experience with Bayesian modeling and probabilistic inference
- Experience building and maintaining scalable data or training pipelines for large-scale ML workloads
- Proficiency in Python and ML frameworks including PyTorch or JAX, with strong software engineering practices (version control, testing, code review)
Preferred Education and Experience - 3-5 years of experience developing and applying ML methods to biomedical or neuropathological data
- Experience with digital pathology workflows, quantitative neuropathology, or computational histology
- Familiarity with multimodal data fusion approaches, including integration of imaging, omics, and clinical data
- Experience with score-based diffusion models, multimodal transformer architectures, or state-space models
- Experience developing or applying causal models of disease or biological processes
- Proficiency with cloud computing platforms and on-premises GPU clusters for large-scale model training, including distributed training frameworks (e.g., PyTorch DDP/FSDP, DeepSpeed, or Ray)
- Experience with containerization and workflow orchestration (e.g., Docker, Kubernetes, Slurm, Nextflow, or Airflow) and with large-scale image formats such as OME-Zarr
- Strong publication record of innovative individual and team scientific contributions
- Excellent written and verbal communication skills and ability to present complex findings to both technical and non-technical audiences
Physical Demands - Fine motor movements in fingers/hands to operate computers and other office equipment; repetitive motion with lab 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.
Travel - Attendance and participation in national and international conferences occasionally required
Additional Comments - Include a cover letter with your application
- **Please note, this opportunity offers relocation assistance**
- **Please note, this opportunity may offer work visa sponsorship**
- This position is funded by and depending upon, a grant award to the Allen Institute . (The position may terminate at the conclusion of the grant)
Annualized Salary Range $129,950 - $162,450 *
* Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable.
Benefits - 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 .
