Building the manufacturing machine behind brain machines.

Rune Labs is a brain data platform which brings precision medicine to neuroscience by making brain data useful at scale. As a Software & MLOps Engineer I find opportunities across data, neuroscience, and business teams to deliver the most impact to our patients, researchers, clinicians and medical device partners.

• Designed and built V2 Python SDK to solve researchers’ struggle to search high density and multilayered patient data (https://github.com/rune-labs/runeq-python).

• Implemented upgrades to core ingestion Python service to transform and correlate neural Deep Brain Stimulation data and Apple Watch health metrics. Managed service deployments with Terraform, and monitored services with Datadog, Honeycomb, and Rollbar.

• Designed and built integration of our data pipeline with Salesforce CRM (utilizing AWS Glue, S3, Terraform and CircleCI) to empower our operations and leadership teams with engagement metrics across patient cohorts.

• Built ML infrastructure with AWS Lambda and Glue, to automate several data exploration and pre-processing tasks, saving several hours of neuroscientists time every week.

• Used Terraform to provision and manage AWS Lambda and Glue jobs, and CircleCI to manage continuous builds and deployments.

• Implemented data ingestion and transformation of Apple Watch HealthKit metrics (ie. sleep, heart rate, gait and exercise metrics) into time indexable data streams with Python.

• Built custom time indexed database on AWS S3 with sharding and locking protocols written in Go to support concurrent time series reads and writes.

• Created several AWS CloudWatch Canaries in Javascript for continuous end to end monitoring of critical authn/authz decisions, data transformations, and service availability.

• Led a team of 5 undergrads to develop a platform for visualizing internal cognitive and emotional states based on multimodal physiological recordings

• Developed real-time streaming and visualization infrastructure for high-density EEG, fNIRS, electrocardiography, electrodermal activity, respiration, blood pressure, facial electromyography, and impedance cardiography, among others modalities.