Teams at Epsilon Health

Build and operate backend services, secure integrations with hospital systems (PACS/RIS/EHR) using DICOM/HL7/FHIR, design cloud networking and site connectivity, run production ETL/data pipelines and CI/CD, ensure HIPAA-compliant handling of PHI, and support ML inference and infrastructure across design-to-deployment lifecycles.

Lead design and end-to-end pretraining of large-scale vision-language foundation models for radiology. Responsibilities include VLM architecture design, multimodal data and task mixture engineering, large distributed training runs, fine-grained visual grounding, pretraining evaluation, dataset curation, and handing off base checkpoints for post-training and RL work. Publish research and establish production best practices for medical VLM pretraining at scale.

Build and maintain ML infrastructure and data systems for medical imaging: distributed training, RL training stack, high-throughput data pipelines, centralized storage, and production serving to accelerate research-to-production iteration.