Triomics

Triomics

Forward Deployed ML Engineer

New York Office

Sponsorship not specified$30k-$60kDetected 34 days ago
PythonMachine LearningData ScienceNLPLLMsRAGAgentic AIAI OrchestrationCommunication

About the role

  • You should be reading real patient charts within your first week - not abstractions of them.
  • Run the evaluation suite on an active customer dictionary and understand the per-variable accuracy breakdown - which variables are easy, which are hard, and why.
  • By end of month one, you should be able to explain the top 5 failure modes in the current extraction pipeline and have an opinion on which ones are fixable with prompt/agent changes vs. which require deeper architectural work.

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
  • Deliver on customer timelines - this means intense sprint periods around customer deliveries followed by iteration and improvement cycles
  • Understand how oncologists document across clinical notes, pathology reports, imaging, and genomic panels.
  • Learn why the same data point (e.g., disease stage, biomarker status, line of therapy) shows up differently across document types and why extraction is hard.
  • Get hands-on with the existing extraction pipeline architecture: how agents are orchestrated, how documents are segmented and classified, how structured JSON is produced, and where the current system fails.
  • Days 30-60: Own a customer delivery end-to-end.
  • Run it yourself: study the customer's data dictionary, map it to the source documents, build or modify the extraction agents, define the evaluation dataset with the annotation team, run precision/recall per variable, and iterate until accuracy targets are met.

Nice to have

  • 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

Compensation

  • $30k-$60k

Company info

  • You should have a point of view on how to standardize extraction pipelines across customers so that new dictionary onboarding takes days, not weeks.
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