Job Description

We’re partnering with a fast-growing healthcare AI company building production-grade software that connects advanced machine-learning models directly into real clinical workflows. This role sits at the intersection of full-stack engineering, cloud infrastructure, and data-intensive systems supporting clinician-facing applications.

You’ll work on systems that handle voice-driven clinical documentation, structured report generation, and backend services that operate at scale in a regulated environment.

What You’ll Work On:

• Build and operate full-stack services supporting clinician workflows and AI-powered applications
• Develop systems that transform voice dictation and model outputs into structured clinical reports
• Create workflows for drafting, editing, and final clinician sign-off
• Design and maintain low-latency, scalable backend services in the cloud
• Work with large volumes of structured and unstructured data, including metadata and model outputs
• Partner closely with machine-learning and infrastructure teams to bring inference into production
• Own services end-to-end: design, implementation, deployment, and ongoing reliability

What We’re Looking For:

• Demonstrated ability to build and ship full-stack systems in production environments
• Experience designing, deploying, and operating cloud-based services (AWS preferred)
• Comfort owning API-driven, data-intensive systems
• Experience working with distributed systems and production reliability concerns
• Familiarity with a modern backend stack, such as:
• AWS (e.g., EC2, S3, ECS/EKS, IAM, monitoring/logging tools)
• Python-based backend services (e.g., FastAPI or similar frameworks)
• Relational databases (e.g., PostgreSQL or equivalent)
• Strong ownership mindset and sound engineering judgment

This team is not credential-driven - they value clear evidence of impact, technical depth, and the ability to own complex systems.

Nice to Have:

• Experience with report generation, document workflows, or structured text pipelines
• Exposure to ML inference pipelines or AI-powered production systems
• Background working in healthcare or other regulated environments

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