Forward Deployed ML Engineer

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

Build and deploy AI agent pipelines that extract structured oncology variables from patient charts for tailor made use cases for pharmaceutical companies and cancer hospitals. You own the full cycle: understanding the customer's data dictionary, studying the source clinical documents, building extraction agents, evaluating accuracy, deploying to production, and iterating until it works. This role requires someone who can go deep into both the agentic layer as well as the clinical domain, coordinate across customer and internal teams, and deliver under deadline pressure.

Responsibilities

• Design and build agentic extraction pipelines that process 500+ page patient charts (clinical notes, pathology reports, imaging reports, genomic panels) and output structured JSON per customer data dictionaries
• Own accuracy end-to-end: define evaluation datasets, run precision/recall analysis per variable, identify failure modes, and improve through agent architecture changes, prompt engineering, fine-tuning, or rule-based post-processing
• Go deep into the clinical source data - read the actual patient charts, understand how oncologists document, learn why certain data points are ambiguous and use that understanding to improve extraction
• Work with the clinical annotation team to build gold-standard datasets and resolve edge cases
• Coordinate with customer data science and clinical teams to clarify dictionary definitions, review output quality, and close accuracy gaps
• Coordinate with internal engineering and infrastructure teams to deploy, scale, and monitor pipelines in production
• Deliver on customer timelines - this means intense sprint periods around customer deliveries followed by iteration and improvement cycles

Requirements

• 2+ years building ML/AI systems in production
• Built and deployed AI agents or multi-step LLM pipelines (not just single-call wrappers) - you should have a clear point of view on agent architectures, tool use, orchestration frameworks, and where they break down
• Strong Python - pipeline code, data processing, infrastructure glue, not just model training scripts
• Practical LLM experience: prompt engineering, fine-tuning, RAG, evaluation design
• Built evaluation frameworks for LLM based document extraction tasks (precision, recall, per-class analysis, error taxonomy)
• Willingness to become a domain expert in oncology data - this role requires going deep into clinical documentation, not just treating it as generic text
• Comfortable owning customer-facing communication alongside technical delivery - you'll talk to customer data science teams, clinical teams, and internal engineering regularly
• Can operate in high-intensity delivery sprints and manage your own time across multiple workstreams

Preferred

• Kept up with the agentic ML landscape - frameworks, patterns, and failure modes in production agent systems
• Clinical or biomedical NLP is a plus but not required - what matters is willingness to go deep into the domain