Research Software Engineer, Brain Data Science Platform (24-Month Fixed-Term)

Stanfordlivetickets; Palo Alto (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Stanford University School of Medicine seeks a Research and Development Scientist and Engineer 1 to serve as a core software engineer for the Sleep Health Data Science Platform. You will build data pipelines ingesting EEG, PSG, wearables, imaging, and EHR data from Stanford and partner hospitals, then de-identify and standardize data for research use.

This hands-on role requires growing domain expertise in medical data and deploying AI models from lab to clinical settings, with emphasis on EEG

Qualifications

• Master's degree or PhD preferred in CS, Biomedical Informatics, Electrical Engineering, Data Science, or related field.
• Experience with EHR data extraction, structuring and analysis (Epic or similar) is highly preferred.
• Three+ years building production data pipelines and backend services, with strong Python proficiency.
• Experience with cloud infrastructure (AWS) and infrastructure-as-code.
• Experience with workflow orchestration (Airflow, Prefect, Nextflow, Snakemake) and containerization (Docker).
• Familiarity with healthcare data standards (HL7/FHIR, DICOM) and de-identification of protected health information.
• Experience deploying ML models into production/clinical settings with monitoring and EHR integration.

Responsibilities

• Build pipelines that ingest EEG, PSG, wearable, imaging, and EHR data from Stanford and partner hospitals.
• Ensure data safety and usability via de-identification and standardization.
• Help develop an AWS-based platform turning data into a research resource for investigators.

Skills

Python

Data pipelines

Cloud infra AWS

Airflow

Docker

CI/CD GitHub Actions

EHR data handling

HIPAA compliance

Time-series data

Model deployment

Education

Master's or PhD in CS/ Biomedical Informatics/ EE/ Data Science

Tools

AWS

Airflow

Docker

GitHub Actions

HL7/FHIR

OHDSI/OMOP

Job description

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, GitHub 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 perform the essential functions of his or her job.

WORKING CONDITIONS:

• May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights ?10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather.
• May require travel.

WORK STANDARDS:

• Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
• Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
• Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu.