Research Software Engineer, Brain Data Science Platform

The Department of Neurology & Neurological Sciences at Stanford University School of Medicine is building a world-class program at the intersection of artificial intelligence and brain health. The laboratory of Dr. M. Brandon Westover develops and deploys AI systems that interpret brain data at scale — EEG, sleep studies, wearable recordings, neuroimaging, and the electronic health record — to improve diagnosis and treatment in epilepsy, neurocritical care, sleep medicine, and neurology broadly.

We are seeking a Research and Development Scientist and Engineer 1 to serve as a core software engineer for the Sleep Health Data Science Platform, a major component of our Brain Data Science Platform. This is a hands‑on engineering role at the center of a rapidly growing clinical research data ecosystem. You will build the pipelines that bring in EEG, polysomnography, wearable, imaging, and EHR data from Stanford and partner hospitals;

make that data safe and usable through automated de‑identification and standardization; and help build the AWS‑based platform that turns it into a research resource for investigators across Stanford and beyond. You will also help move AI models out of the lab and into clinical use, with particular emphasis on AI‑assisted EEG interpretation.

This role suits an engineer who wants to go deep on a domain. You will be expected to become a genuine expert in medical data — how it is generated, what it means clinically, and where it goes wrong — and to bring that expertise to bear on the architecture.

DESIRED

QUALIFICATIONS:

• Master's degree or PhD preferred, in Computer Science, Biomedical Informatics, Electrical Engineering, Data Science, or a related technical field.
• Experience working with electronic health record (EHR) data strongly preferred, including extraction, structuring, and analysis of clinical data from systems such as Epic, and familiarity with clinical data warehouses.
• Three or more years building production data pipelines and backend services, with strong proficiency in Python.
• Experience with cloud infrastructure, preferably AWS (S3, Lambda, Batch/ECS, RDS, IAM), and with infrastructure‑as‑code.
• Experience with workflow orchestration (Airflow, Prefect, Nextflow, Snakemake, or similar), containerization (Docker), and version control and CI/CD (Git, Git Hub Actions).
• Experience with healthcare data standards and formats — EDF/EDF+, DICOM, HL7/FHIR, OMOP/OHDSI — and with de‑identification of protected health information.
• Experience working with large physiological time‑series data (EEG, PSG, ECG, actigraphy, or wearable sensor streams) strongly preferred.
• Familiarity with HIPAA, IRB, and data use agreement requirements governing human subjects research data.
• Experience deploying machine learning models into production or clinical settings, including model serving, monitoring, and EHR integration, desirable.
• Demonstrated ability to work independently, scope ambiguous problems, and deliver reliable systems.
• Strong written and verbal communication skills, and genuine interest in becoming a domain expert in clinical neurophysiology and medical data.

PHYSICAL REQUIREMENTS*:

• Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
• Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully.
• Rarely kneel/crawl, climb (ladders, scaffolds, or other), reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to…