What you’ll do • Design and implement secure cloud pipelines that ingest very large scan datasets (multi-terabyte), reliably and resumably. • Build orchestration for GPU-accelerated reconstruction and analysis with strong retry semantics, idempotency, and cost controls. • Define end-to-end data lifecycle for medical imaging: raw vs intermediate vs derived artifacts, retention policies, and reproducibility. • Implement security + compliance primitives appropriate for HIPAA/PHI: encryption in transit/at rest, key management, least privilege, audit logs, and access reviews. • Build operational tooling: monitoring, alerting, runbooks, and incident-driven improvements for a growing device fleet. What we’re looking for • Strong experience with cloud batch/queueing/orchestration, storage systems, and data pipeline reliability. • Experience shipping production systems that handle large data volumes and failure-prone networks. • Practical security mindset (least privilege, secrets, audit logging) and comfort operating in compliance-constrained environments. Useful experience • Building reliable data pipelines at scale (queues/orchestration, resumable uploads, GPU batch execution) with strong observability. • Security + privacy by default: encryption, least-privilege access, auditing, and practical HIPAA/PHI guardrails. • Owning the “boring” backend details that keep a lean team moving: schemas/migrations, cost controls, retries, and runbooks. • Understanding compute tradeoffs across hardware options, and specifying appropriate cloud resources.