Mid/Senior Backend Engineer

About the role

Dyania Health builds Synapsis AI, which extracts clinically relevant insights from electronic medical records to support patient care and clinical research. The Mid/Senior Backend Engineer works on event-driven backend components powering Synapsis AI's pipelines across multiple deployments, with at least 4 days per week in the office.

Key responsibilities

• Design, build and maintain event-driven backend components powering Synapsis AI's pipelines.
• Author and extend study-specific Python code that encodes clinical criteria, working with clinicians to turn protocols into executable matching logic.
• Develop and optimize data models and storage layers for structured clinical data and unstructured notes.
• Implement and tune long-running, retry-safe workflows coordinating CPU-bound and GPU-bound workers on shared infrastructure.
• Contribute to the algorithmic reasoning engine that matches patient records against complex, time-sensitive trial criteria, with the Clinical and AI Research teams.
• Troubleshoot and improve performance, reliability and observability of production deployments.
• Document system designs, workflow contracts and study definitions so new studies can be onboarded and new engineers can ramp up quickly.

Required profile

• Proven experience as a backend software engineer on data-intensive systems, ideally in healthcare, bioinformatics or a similarly regulated domain.
• Strong proficiency in Python, including async patterns and services processing large data volumes.
• Experience with event-driven or workflow-based architectures such as queue/worker systems, pub/sub or durable workflow engines.
• Solid experience with ETL, data transformation and integration pipelines over heterogeneous sources.
• Familiarity with SQL (e.g. MSSQL, Postgres) and NoSQL (MongoDB) stores, including schema and query-level performance tuning.
• Experience with containerization (Docker), production services and CI/CD pipelines.
• Strong debugging and systems-thinking skills, able to reason about partial failures, idempotency and retry semantics.
• Clear written and verbal communication, comfortable collaborating across clinical, AI research and frontend teams.

Nice to have

• Temporal or a comparable durable workflow engine (Airflow, Prefect, Argo Workflows, Cadence, Step Functions).
• Orchestrating GPU-bound inference alongside CPU-bound services in a shared-resource environment.
• Healthcare data standards (HL7, FHIR) or EHR analytics stores (Epic Caboodle, Clarity).
• Vector databases or retrieval pipelines (pgvector, FAISS).
• Deploying to on-premises or private-cloud hospital environments, including HPC/Slurm or air-gapped contexts.
• Authoring DSLs or declarative specifications readable by non-engineers.

What we offer

• Competitive salary.
• Training and professional development support.
• Work from home.
• Private health insurance.
• Subsidized daily meals.
• Company laptop.