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 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. 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 What Success Looks Like in the First 90 Days
Days 1-30: Learn the stack, the data, and the domain. You should be reading
real patient charts within your first week - not abstractions of them.
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. 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. Days 30-60: Own a customer delivery
end-to-end. Pick up an active customer workstream -- a new dictionary, a new
tumor type, or an accuracy improvement cycle on an existing delivery. 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. You should be coordinating directly
with the customer's data science team on edge cases and definition
ambiguities. Simultaneously, you should be identifying patterns across
customer dictionaries. Days 60-90: Ship improvements and have an opinion on
every decision Deliver measurable accuracy improvements on your owned
workstream - concrete numbers, not vibes. Document the pipeline architecture,
evaluation methodology, and customer-specific decisions well enough that
another engineer can pick up the work. You should have a point of view on how
to standardize extraction pipelines across customers so that new dictionary
onboarding takes days, not weeks. 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