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Technical Support Engineer

Triomics
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New York, NY$125k - $150kFull-timePosted 6mo ago

About the role

Role focused on day-to-day production health and customer issue resolution across Triomics deployments, ranging from data ingestion failures and document processing pipeline issues to AI extraction accuracy problems and UI bugs.

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

Qualifications

  • 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

Skills

Required:SQLPythonCommunication

Benefits

Last updated Oct 7, 2026. Interested? Apply on the company site.
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About Triomics

Generative AI for Oncology EHRs

Triomics is helping cancer care providers by reducing the burden associated with manually reviewing patient charts. Utilizing our OncoLLM™—a family of oncology-focused language models—we are able to accelerate clinical trial enrollment, power quality improvement projects, and address many other key strategic priorities.

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