Rochester, Minnesota
I am a software engineer at Mayo Clinic IT focused on machine learning infrastructure and internal platforms. I build GPU ready compute, distributed training environments, and cloud native services that help teams train and deploy ML workloads reliably.
My work spans Terraform and GCP automation, SLURM based GPU clusters, observability for ML systems, and secure API services, with additional experience building models and algorithms for time series and biomedical signals. I like working on the infrastructure that makes ML teams faster, safer, and more effective.
Software Engineer, Generative AI Program · Mayo Clinic IT
M.Sc. Biomedical Engineering and Physiology · Mayo Clinic Graduate School
B.Sc. Industrial Engineering and Management · Ben Gurion University
I focus on core pieces of the ML infrastructure stack: GPU compute, orchestration, pipelines, metadata, and observability. My goal is to make these systems reliable and easy to use so ML and data science teams can ship work faster and with fewer operational surprises.
Designing and operating GPU enabled environments using GCP, Managed Instance Groups, SLURM, and MIG based partitioning. Automating provisioning and configuration to support training, experiments, and shared research workloads.
Using Terraform, Ansible, and CI/CD to build repeatable infrastructure, internal services, and platform components for ML teams. Emphasis on clear patterns, security, and maintainability.
Building pipelines and schemas that keep datasets, experiments, and artifacts organized, especially for multimodal and time series workloads. Supporting FAIR principles and reproducible workflows across teams.
Integrating telemetry for GPUs, jobs, and services using NVIDIA DCGM and cloud monitoring. Exposing metrics and health signals that make it easier to debug issues, plan capacity, and keep ML systems healthy over time.
Core technologies and areas I work with across ML infrastructure, platforms, and cloud engineering.
Mayo Clinic · Generative Artificial Intelligence Program
Remote · Jan 2024 to PresentI build and operate ML infrastructure for the Generative AI Program, including GPU compute environments, SLURM automation, and distributed training support on GCP. I developed a GPU observability stack using Cloud Ops and NVIDIA DCGM to improve reliability and diagnostics, and maintain internal FastAPI services for secure model access and credential management integrated with GCP Secret Manager and infrastructure as code workflows using Terraform and Ansible.
Mayo Clinic · Multimodal Neuroimaging Laboratory
On-site · May 2021 to Jan 2024I built high throughput pipelines for diffusion MRI and intracranial EEG processing using Python, Linux, and distributed workflows. I led development of HED SCORE, an open source EEG metadata framework adopted by international research teams, and designed tools for multimodal data integration, structured metadata management, and quality control across large neuroimaging datasets in collaboration with clinicians and data scientists.
Mayo Clinic · Bioelectronics Neurophysiology and Engineering Laboratory
On-site · May 2018 to May 2021I worked on ML for long duration EEG and wearable sensor data, developing LSTM based seizure prediction models, integrating real time ML components into clinical grade monitoring systems, and building ingestion, preprocessing, and feature extraction pipelines for large biosignal datasets in collaboration with neurology and engineering teams.
Mayo Clinic · Bioelectronics Neurophysiology and Engineering Laboratory
On-site · May 2016 to May 2018I prototyped closed loop neuromodulation systems that combined hardware signals with real time software control and engineered backend tools for structured patient data tracking, automation, and biosignal analysis while supporting research teams with software development, data workflows, and database design for experimental studies.
How I think about building infrastructure and platforms for machine learning teams.
I build infrastructure that teams can understand and rely on without needing to know every internal detail. Good platforms and tools should make it easier to do the right thing by default and lower the barrier to trying new ideas.
In high-stakes domains like healthcare, clarity and explainability matter just as much as performance - reliable infrastructure helps people do things safely and confidently.
Internal service for managing API keys and access to language model providers. Integrates with GCP Secret Manager, includes audit logging and scheduled rotation, and provides a simple admin UI for platform and security teams.
Unified GPU telemetry stack that collects metrics and health signals from GPU nodes and exposes them in dashboards and alerts. Enables teams to track utilization, debug issues, and plan capacity for ML workloads.
Built an automated system for creating and managing SLURM clusters on demand for research teams, using instance templates and infrastructure automation to standardize configuration and simplify lifecycle operations.
Machine readable metadata framework for EEG annotations that supports FAIR aligned data sharing and large scale analysis. Designed to make neurophysiology datasets easier to reuse across labs and tools.
Python based pipelines for diffusion MRI and intracranial EEG preprocessing and analysis. Built to support reproducible studies and collaboration between engineering and clinical research teams.
Development of models and algorithms for long-duration EEG and wearable sensor data, including LSTM-based seizure prediction, anomaly detection, forecasting of biomedical time series, and signal artifact reduction.
System for seizure risk forecasting using EEG and wearable data. Combines feature engineering, temporal modeling, and long horizon prediction for real world monitoring scenarios.
View on Google ScholarConnect with me to discuss roles or collaborations related to ML infrastructure, platforms, and cloud native ML systems.
Open LinkedIn ProfileBrowse my publications and patent work related to seizure prediction, EEG metadata, and multimodal neuroimaging.
View Google ScholarI am focused on ML infrastructure and cloud ML engineering, and open to learning about teams working in these areas where my experience may be a good fit.
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