TRIOMICS
Technical Support Engineer
Location
New York Office
Employment Type
Full time
Location Type
Hybrid
Department
Support
Compensation
$125K – $150K
Overview
Application
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
Own day-to-day production health and customer issue resolution across our deployments. Issues here range from data ingestion failures and document processing pipeline errors to AI extraction accuracy problems and clinical UI bugs. You investigate, resolve what you can, escalate the rest with full context, and communicate clearly with clinical teams who depend on the platform daily.
Responsibilities
Investigate production issues end-to-end: trace across EHR data ingestion, document processing pipelines, AI extraction services, and application layer to classify root cause and resolve or escalate with full diagnostic context
Monitor production systems across multiple customer deployments and cloud environments - catch pipeline failures, data quality drops, and extraction accuracy regressions before customers report them
Communicate with clinical users (research coordinators, tumor registrars, data managers) - provide clear status updates and honest ETAs in non-technical language
Build support infrastructure: define triage workflows, write runbooks for common failure modes (document ingestion errors, refresh inconsistencies, model output issues), set up monitoring dashboards and alerting
Identify recurring issue patterns and translate them into product or engineering priorities
Train additional support engineers as the function scales
Requirements
3+ years in technical support, solutions engineering, or production operations at a SaaS or data platform company
Can query SQL databases, read application logs through Grafana or Temporal, navigate AWS or Azure infrastructure, and trace issues through a multi-service backend
Strong written communication for both technical teams and non-technical clinical users
Comfortable building processes from scratch
Preferred
Built support tooling (ticketing, monitoring, runbooks) at an early-stage company
Healthcare technology experience is a plus but not required
Experience with data pipelines - can distinguish data quality issues from application bugs
Familiarity with Kubernetes and containerized deployments
On-call and incident response experience
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 leading cancer centers including Memorial Sloan Kettering, Mount Sinai, and Yale Cancer Center. We have grown 10x in the last year and process millions of oncology medical documents monthly.
Our investors include Battery Ventures, Lightspeed, General Catalyst, Nexus Venture Partners, and Y Combinator.
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Listed by Triomics for a position based in the United States. Employers on this board attest they are hiring domestically.
TRIOMICS
Platform Engineer
Location
New York Office
Employment Type
Full time
Location Type
Hybrid
Department
Engineering
Compensation
$150K – $200K
Overview
Application
Job Description:
This role spans backend product engineering and infrastructure. You'll build backend services and application features, and also own the cloud infrastructure, deployments, and CI/CD that keeps them running in production. The platform processes millions of clinical documents monthly across multi-tenant deployments in customer as well as Triomics cloud environments, with GPU infrastructure serving AI extraction models. We need someone who can write application code in the morning and debug a Kubernetes deployment issue in the afternoon.
What Success Looks Like in the First 90 Days
Days 1-30: Map the entire infrastructure and find what's fragile.
Get access to every deployment - AWS, Azure, customer-hosted environments. Understand the full topology: how Kubernetes clusters are configured, how GPU nodes serve models, how document pipelines move data from EHR ingestion to extraction to structured output. Your first job is to understand what is already built, where the sharp edges are, and what breaks when load spikes or a deployment goes sideways. By end of month one, you should have a written map of every production environment, know which deployments are most fragile, and have identified the top 3 infrastructure risks.
Days 30-60: Own production stability and start shipping backend services.
Take ownership of at least one customer deployment end-to-end - monitoring, alerting, incident response. Set up observability that catches pipeline failures and data quality regressions before customers report them (today, customers often find issues first). Simultaneously, pick up a backend product feature - patient data processing, document pipeline improvement, or a platform feature the product team needs. Ship it. The goal is to make sure you can context-switch between infra firefighting and product engineering.
Days 60-90: Standardize deployments and Monitor Everything.
Document deployment runbooks, automate what's manual, and build CI/CD improvements that make releases safer and faster. You should have a clear plan for what the infrastructure needs to look like to support 2-3x the current customer count without adding headcount proportionally.
Responsibilities
Build and ship infrastructure services that power our product - document pipelines, application logic, and platform features
Own cloud infrastructure and deployment pipelines across both Triomics and customer environments (AWS, Azure)
Manage Kubernetes clusters, containerized services, CI/CD, and release processes including GPU node management for model serving
Build monitoring, alerting, and observability across production deployments - we process millions of documents and need to catch pipeline failures, data quality regressions, and infrastructure issues before customers do
Debug and resolve production issues end-to-end - from application-layer bugs to infrastructure failures
A significant portion of our engineering team is offshore and this role requires working with that team as well on architecture decisions, code reviews, and production stability
Requirements
3+ years as a platform/infrastructure engineer at a startup or growth-stage company
Strong backend engineering: can design, build, and ship production services
Comfortable across the infrastructure stack: cloud (AWS or Azure), Kubernetes, Docker, CI/CD, networking, monitoring
Experience managing production deployments and debugging issues across application and infrastructure layers.
Can context-switch between writing product code and doing infra/ops work without treating either as out of scope of their job
Preferred
Experience with data-heavy applications - document processing pipelines, batch and real-time data workflows
Worked with ML/AI systems in production - model serving, GPU infrastructure, pipeline orchestration
Built infrastructure at an early-stage company where you were one of few engineers owning the full stack
Familiarity with building third party integrations in product is a plus
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Triomics for a position based in the United States. Employers on this board attest they are hiring domestically.
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
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
Listed by Triomics for a position based in the United States. Employers on this board attest they are hiring domestically.
Job Description:
This role spans backend product engineering and infrastructure. You'll build backend services and application features, and also own the cloud infrastructure, deployments, and CI/CD that keeps them running in production. The platform processes millions of clinical documents monthly across multi-tenant deployments in customer as well as Triomics cloud environments, with GPU infrastructure serving AI extraction models. We need someone who can write application code in the morning and debug a Kubernetes deployment issue in the afternoon.
WHAT SUCCESS LOOKS LIKE IN THE FIRST 90 DAYS
Days 1-30: Map the entire infrastructure and find what's fragile.
Get access to every deployment - AWS, Azure, customer-hosted environments. Understand the full topology: how Kubernetes clusters are configured, how GPU nodes serve models, how document pipelines move data from EHR ingestion to extraction to structured output. Your first job is to understand what is already built, where the sharp edges are, and what breaks when load spikes or a deployment goes sideways. By end of month one, you should have a written map of every production environment, know which deployments are most fragile, and have identified the top 3 infrastructure risks.
Days 30-60: Own production stability and start shipping backend services.
Take ownership of at least one customer deployment end-to-end - monitoring, alerting, incident response. Set up observability that catches pipeline failures and data quality regressions before customers report them (today, customers often find issues first). Simultaneously, pick up a backend product feature - patient data processing, document pipeline improvement, or a platform feature the product team needs. Ship it. The goal is to make sure you can context-switch between infra firefighting and product engineering.
Days 60-90: Standardize deployments and Monitor Everything.
Document deployment runbooks, automate what's manual, and build CI/CD improvements that make releases safer and faster. You should have a clear plan for what the infrastructure needs to look like to support 2-3x the current customer count without adding headcount proportionally.
RESPONSIBILITIES
- Build and ship infrastructure services that power our product - document pipelines, application logic, and platform features
- Own cloud infrastructure and deployment pipelines across both Triomics and customer environments (AWS, Azure)
- Manage Kubernetes clusters, containerized services, CI/CD, and release processes including GPU node management for model serving
- Build monitoring, alerting, and observability across production deployments - we process millions of documents and need to catch pipeline failures, data quality regressions, and infrastructure issues before customers do
- Debug and resolve production issues end-to-end - from application-layer bugs to infrastructure failures
- A significant portion of our engineering team is offshore and this role requires working with that team as well on architecture decisions, code reviews, and production stability
REQUIREMENTS
- 3+ years as a platform/infrastructure engineer at a startup or growth-stage company
- Strong backend engineering: can design, build, and ship production services
- Comfortable across the infrastructure stack: cloud (AWS or Azure), Kubernetes, Docker, CI/CD, networking, monitoring
- Experience managing production deployments and debugging issues across application and infrastructure layers.
- Can context-switch between writing product code and doing infra/ops work without treating either as out of scope of their job
PREFERRED
- Experience with data-heavy applications - document processing pipelines, batch and real-time data workflows
- Worked with ML/AI systems in production - model serving, GPU infrastructure, pipeline orchestration
- Built infrastructure at an early-stage company where you were one of few engineers owning the full stack
- Familiarity with building third party integrations in product is a plus
Listed by Triomics for a position based in the United States. Employers on this board attest they are hiring domestically.
Job Description:
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
Listed by Triomics for a position based in the United States. Employers on this board attest they are hiring domestically.
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