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AI Federated Learning Coordinator

James Yamamoto

Full-time · Job · New_York

About the role

Hospitals want to train models on patient data without sharing it across institutions. Federated learning makes this possible — each hospital trains locally, and only model updates travel between sites. But coordinating training across 30 hospitals with different IT infrastructure, data formats, and compliance requirements is an engineering and logistics challenge that most federated learning papers conveniently ignore. You'll coordinate the entire federated training lifecycle: onboarding new hospital sites with our training client, managing communication protocols between heterogeneous network environments, detecting and handling stragglers and failed rounds, and aggregating model updates using differential privacy guarantees that satisfy each institution's IRB requirements. Federated learning in healthcare isn't just a distributed systems problem — it's a trust problem. Each hospital needs proof that their data stays private, that the aggregated model benefits their patients, and that the coordination overhead is worth the performance improvement over training on their own data alone.

Responsibilities

  • Coordinate federated training across 30+ hospital sites with heterogeneous infrastructure
  • Manage secure communication protocols for model update aggregation
  • Implement differential privacy guarantees satisfying institutional IRB requirements
  • Handle stragglers, failed rounds, and non-IID data distribution across sites
  • Report federated model performance improvements to participating institutions

Requirements

  • 5+ years in ML engineering with distributed or federated learning experience
  • Strong PyTorch skills for federated training frameworks
  • Kubernetes and Docker expertise for multi-site deployment
  • Understanding of differential privacy and secure aggregation protocols
  • Experience working with healthcare institutions and compliance requirements

Benefits

  • Fully remote — US East Coast hours
  • Senior compensation with equity
  • $3,500 annual conference budget
  • Advancing privacy-preserving healthcare AI

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes aws statistics

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