Teams at Triomics

Build and maintain ML infrastructure and data pipelines for clinical document processing, deploy and monitor ML/LLM systems, support evaluation and annotation workflows, create internal tooling (e.g., Streamlit), and automate ML workflows to improve reproducibility and reduce operational burden.

Build and evaluate applied NLP and LLM systems for clinical text (oncology). Develop prototypes and production components for extraction, RAG, prompts, fine-tuning, and evaluation. Collaborate with clinical data and evaluation teams, write tested Python code, analyze model failures, and improve model robustness and reliability.

Own production health and customer issue resolution across deployments: investigate and trace issues across EHR ingestion, processing pipelines, AI extraction services, and application layers; monitor systems and set up alerts; build runbooks and triage workflows; communicate status to clinical users; identify recurring problems and drive product/engineering fixes; train support engineers.