About the role

About Triomics

Triomics is building the agentic AI layer for oncology EHRs. Cancer hospitals spend billions on highly trained staff manually reading unstructured patient records - pathology reports, clinical notes, genomic panels - to power workflows like trial matching, registry curation, visit prep, and quality reporting. We replace that manual work with task-driven AI agents that sit inside the EMR and process records at scale, in real time. Our platform is trusted by the 4 of the top 10 Best Hospitals for Cancer by U.S.News and several of the largest community practices. We have grown 10x in the last year and process millions of oncology medical documents monthly. Our investors include Lightspeed, General Catalyst, Nexus Venture Partners and Y-Combinator.

Role

Own day-to-day production health and customer issue resolution across our deployments. Issues here range from data ingestion failures and document processing pipeline errors to AI extraction accuracy problems and clinical UI bugs. You investigate, resolve what you can, escalate the rest with full context, and communicate clearly with clinical teams who depend on the platform daily.

Responsibilities

Investigate production issues end-to-end: trace across EHR data ingestion, document processing pipelines, AI extraction services, and application layer to classify root cause and resolve or escalate with full diagnostic context
Monitor production systems across multiple customer deployments and cloud environments - catch pipeline failures, data quality drops, and extraction accuracy regressions before customers report them
Communicate with clinical users (research coordinators, tumor registrars, data managers) - provide clear status updates and honest ETAs in non-technical language
Build support infrastructure: define triage workflows, write runbooks for common failure modes (document ingestion errors, refresh inconsistencies, model output issues), set up monitoring dashboards and alerting
Identify recurring issue patterns and translate them into product or engineering priorities
Train additional support engineers as the function scales

Requirements

3+ years in technical support, solutions engineering, or production operations at a SaaS or data platform company
Can query SQL databases, read application logs through Grafana or Temporal, navigate AWS or Azure infrastructure, and trace issues through a multi-service backend
Strong written communication for both technical teams and non-technical clinical users
Comfortable building processes from scratch

Preferred

Built support tooling (ticketing, monitoring, runbooks) at an early-stage company
Healthcare technology experience is a plus but not required
Experience with data pipelines - can distinguish data quality issues from application bugs
Familiarity with Kubernetes and containerized deployments
On-call and incident response experience