Advance every cancer decision. Backed by General Catalyst.
About the role
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
What they're looking for
- 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
More about this role
Build and deploy AI agent pipelines that extract structured oncology variables from unstructured patient documents 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.
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...
Browse similar: AI jobs · AI startup jobs · Startup jobs · Remote jobs