Triomics

About Triomics Triomics builds AI-powered clinical workflows for oncology care and research. We partner with leading cancer centers and health systems, deploying deeply integrated solutions that require both deep technical integration and clinical adoption. We’re scaling quickly and building the team that will define our next chapter. The Role We’re hiring an Account Manager to be the owner of the relationship with our customers. This is a customer success and account ownership role—not a quota-carrying upsell seat. You will be the single most informed person on the health of your accounts, from the moment a deal closes, through integration planning and go-live, and into perpetuity. You will be the quarterback of the Triomics customer experience: the project manager who coordinates our internal teams and the customer’s teams to accelerate integrations and go-lives, and the proactive operator who keeps a constant pulse on adoption, value realization, and account health long after launch. You’ll work hand-in-hand with our Forward Deployed Engineering (FDE) team and our Clinical Navigators, pulling in FDEs to tune the AI pipeline and platform to customer-specific needs, and Navigators to supplement end-user training, so that every account gets the most out of Triomics. Our users are clinicians: physicians, nurses, and clinical research coordinators (CRCs). The best person for this role speaks their language, understands their day-to-day, and earns their trust quickly. What You’ll Do Own the end-to-end customer relationship Serve as the primary point of contact and trusted advisor for your accounts from closed-won through go-live and into steady-state—the “mini-CEO” for your book of business Build deep, durable relationships with customer stakeholders across clinical, research, IT, and administrative functions Maintain the most up-to-date pulse on the health of every account: adoption, sentiment, risks, and opportunities to deliver more value Quarterback integration and go-live Act as the project manager / quarterback from the Triomics side of every integration and go-live, coordinating internal teams (FDE, Clinical, Product) and the customer’s teams to accelerate timelines Build and manage integration project plans, milestones, and timelines; drive accountability on both sides to keep deployments on track Partner with Forward Deployed Engineers to scope and prioritize customer-specific adjustments to the AI pipeline and platform Partner with Clinical Navigators to design and deliver end-user training that drives adoption among physicians, nurses, and CRCs Drive adoption, value, and account health post-go-live Dive into analytics and reporting to understand user adoption, workflow impact, and the real-world success of the technology Proactively identify where an account is underperforming or where value is being left on the table—then game-plan a solution and execute it yourself or marshal the right internal team to deliver it Propose and help build new reporting, KPIs, and dashboards that sharpen our (and the customer’s) understanding of adoption and outcomes Establish a consistent operating cadence with each account (e.g., business reviews, health checks, success planning) Surface product feedback and customer needs back to Product and Engineering as a structured, prioritized signal Be a force multiplier across the company Help build the repeatable playbooks, templates, and processes that let our customer delivery model scale as we grow from a handful to 25+ enterprise logos Coordinate seamlessly with Commercial on handoffs, expansion readiness, and renewals What Success Looks Like (First 90 Days) You’ve built strong relationships with the stakeholders across your accounts and have a clear, current read on the health of each one You’re running integration / go-live project plans independently and visibly accelerating timelines You’ve established a working rhythm with FDEs and Clinical Navigators and have pulled them in

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. The Role We have live deployments at some of the best cancer centers in the world, and multiple products in production at scale. We are looking for a Director of Engineering to own that. You will run all of product and platform engineering - the application teams behind the platform, infrastructure, and reliability function; and frontend. The engineering leads across these areas will report to you. You will report to the CTO and take day-to-day ownership of how engineering plans, builds, ships, and operates.Your job is to raise the bar. This is not a hands-off role. You'll set technical direction, sit in design reviews, get into production incidents, and solve problems for strong senior engineers - while building the process, and standards that let a ~20-person team ship like a much larger one. What You'll Own All engineering execution. Application engineering, platform and infrastructure, reliability, and frontend. You own what ships, when it ships, and how good it is. Production reliability and quality. Our customers are clinicians making care and research decisions on our output. Every error erodes trust, and clinical users don't give second chances. Own the observability across multi-tenant deployments, catching pipeline failures and data-quality regressions before customers do, incident response, and a real testing and code-quality culture. Customer go-lives. Onboarding a new cancer center is high-stakes. Make deployment to new customer environments - across multiple clouds and private/single-tenant setups, each with its own security and compliance requirements - fast, repeatable, and boring. The engineering operating system. Planning and execution cadence, design and code review, release process, on-call, and the bar for what "done" means. Replace heroics and tribal knowledge with systems. The team and the bar. Set leveling and promotion standards, run performance honestly, and hire across the org as we scale. Define the structure as the team grows from a handful of pods into a real engineering organization. Partnership with ML and Product. Work with the ML team on serving, evaluation, and accuracy benchmarks, and with Product on turning strategy into reliable, shippable software. What Success Looks Like in the First 90 Days Days 1–30: Learn the systems, the products, the team. Read the architecture and the code. Use every product. Look at raw extraction output against source documents and understand where the platform is fragile and why. Sit in on a customer deployment and a production incident. Meet every engineer, every lead, and the ML and product leadership. By the end of month one you should be able to name the three things most hurting reliability and delivery speed, and have a candid read on the strengths and gaps of the engineering team and its leads. Days 30–60: Take ownership and set the operating system. Take over the day-to-day running of engineering from the CTO - planning, reviews, prioritization, and delivery accountability. Stand up the operating cadence: how teams plan and commit, how code and designs get reviewed, how releases ship, and how incidents are handled. Pick the single biggest reliability or delivery problem and personally drive

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 t

About Triomics Triomics is building the modern technology stack for oncology trial sites and investigators that unifies the workflows of clinical care and clinical research, moving the healthcare industry closer to the vision of Clinical Research as a Care Option. Our platform, built on our proprietary oncology-focused large language model (OncoLLM™) eliminates the operational inefficiencies in patient recruitment, data curation, and other laborious tasks involved in clinical research—enabling the generation of high-quality data and speeding up clinical trials. We’re scaling quickly and building the team that will define our next chapter. The Role We’re hiring an Oncology Clinical Navigator to own the adoption, training, and enablement of the Triomics platform at our customer accounts. This is an internal Triomics role that partners deeply with our customers—the physicians, nurses, and clinical research coordinators (CRCs) who use our software every day—to ensure the platform is understood, embraced, and used to its full potential. The ideal person knows firsthand what it’s like to be in our end customers’ shoes. You’ve worked in oncology care or clinical research, you understand the realities of a busy cancer center, and you can meet clinicians and research staff where they are. You’ll use that credibility to devise, implement, and support the adoption of a powerful new technology—translating between the clinical world and the platform, and making change feel achievable rather than disruptive. You’ll work in close partnership with our Account Managers, who own the overall customer relationship, and our Forward Deployed Engineering (FDE) team, who tune the AI pipeline and platform to customer-specific needs. You’ll be the clinical expert on the ground who makes that adoption real. What You’ll Do Drive end-user adoption and training Design and deliver training for the clinical end users of the Triomics platform—physicians, nurses, and CRCs—tailored to their workflows and level of comfort with technology Build scalable, reusable enablement assets (onboarding tracks, quick-reference guides, workflow walkthroughs, release-update training) that reduce repetition and accelerate ramp Serve as the trusted clinical adoption partner at your assigned accounts—meeting users where they are and earning their confidence in a new way of working Partner deeply with customer accounts and their peers Embed with the clinical and research teams at your assigned cancer centers to understand their workflows, pain points, and goals Devise and implement account-specific adoption plans—identifying the right champions, sequencing rollout, and supporting users through change Act as a peer and translator between clinical end users and Triomics, helping each side understand the other Provide hands-on, high-touch support through go-live and into steady-state usage Work in concert with Account Management and Forward Deployed Engineering Partner with Account Managers as the clinical adoption arm of the customer relationship; keep them informed on user sentiment, adoption blockers, and opportunities Surface customer-specific needs to Forward Deployed Engineers so the AI pipeline and platform can be adjusted to better fit each account Flag recurring user friction and feature requests back to Product and Engineering as structured, prioritized feedback Champion the product and improve the playbook Provide feedback on the user interface and capabilities of the platform to maximize both clinician usability and institutional goals Help build the repeatable training and adoption playbooks that let our customer delivery model scale as we grow from a handful to 25+ enterprise logos Continuously refine how we measure and improve adoption across accounts What Success Looks Like (First 90 Days) You’ve built credibility and strong relationships with the clinical and research teams at your assigned accounts You’ve delivered effective training that has measurably mo

About Triomics Triomics is building the AI infrastructure for modern oncology. Across the U.S., cancer care providers rely on highly trained staff to manually review unstructured patient records such as pathology reports, clinical notes, genomic panels, and scanned faxes to support workflows like matching patients to the right clinical trials, preparing for upcoming visits, and quality reporting. We replace that manual effort with task-driven AI agents embedded directly into clinical workflows, automatically processing medical records at scale and in real time. Our platform is trusted by 4 of the top 10 Best Hospitals for Cancer ranked by U.S. News, along with several of the largest community oncology practices. We have grown 10x over the last year and now process millions of oncology medical documents each month. The Role We’re hiring a Talent Recruiter to help us scale across roles like Customer Success/Account Management, forward-deployed engineering & integrations, GTM (provider + pharma), AI research, and other core business functions. This is not a “post jobs and review inbound” seat. We’re looking for someone who is high-output, process-rigorous, and AI-native—someone who uses modern recruiting tech and automation to find, prioritize, and engage the right candidates quickly, while leveraging leadership, team, and investor networks strategically as an accelerant (not the foundation). What You’ll Do Own full-cycle recruiting for priority roles Run intake/kickoff meetings with hiring managers; define success profiles, scorecards, sourcing strategy, and timelines Source proactively via LinkedIn, outbound email, communities, referrals, events, and AI-powered tools—especially for niche roles (e.g., clinical implementation, healthcare IT integration, clinical workflow account management, AI research, etc.) Screen candidates, manage interview loops, collect feedback, and keep processes moving fast Drive offer process (comp calibration, closing strategy, negotiation support) and ensure a high-close-rate candidate experience Build a modern recruiting engine Stand up repeatable sourcing playbooks by function (CS, AM, FDE/Integrations, Sales, AI Research) AI-enabled sourcing at scale: use modern talent intelligence + sourcing tools to generate high-quality slates quickly (e.g., SeekOut, hireEZ, Findem, Gem) and continuously improve pipeline quality via testing/iteration AI-accelerated screening & signal capture: leverage structured scorecards plus AI-assisted workflows (within your ATS and/or interview intelligence tools) to tighten screening, debriefs, and decision-making without sacrificing candidate experience. AI-assisted outreach & personalization: create high-conviction, persona-specific outreach sequences (role-based messaging, adjacent-company mapping, follow-up automation) while keeping a high-touch tone for senior and niche candidates. Custom workflows (bonus): build lightweight custom agentic workflows to speed intake docs, sourcing queries/Booleans, outreach drafts, and candidate “short memos” for hiring managers—practical automation that measurably improves throughput. Track funnel metrics (time-to-fill, pass-through rates, source performance, close rates) and recommend changes Be a strategic partner to leadership Advise on leveling, role design, and market realities (NYC vs remote, title calibration, compensation bands) Help prioritize hiring plans to ensure speed to close Coordinate scheduling and candidate comms with a high-touch, white-glove approach—especially for senior hires What Success Looks Like (First 90 Days) You’ve built strong relationships with leadership and the core hiring managers and can run tight intakes independently You’ve shipped hires in at least 2–3 priority functions (e.g., AM/CS, ClinOps, FDE/Integrations, Sales) You’ve stood up a repeatable sourcing motion and improved our speed and signal quality. Candidates consistently rate their experience highly (fast, clear, respectful, high-con

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 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 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 se