About Triomics:Triomics helps oncology organizations scale clinical research and high-quality data creation without scaling headcount. Our AI platform - powered by OncoLLM- automates the operational work behind trial screening, eligibility interpretation, and oncology data abstraction across Prism (trial matching), Harmony (registry + RWE curation), and Symphony (clinician copilot). Built for real-world hospital workflows, Triomics integrates with EHRs and CTMS systems and is deployed with leading academic cancer centers to improve speed, consistency, and audit-ready clinical accuracy. Job Description:Part of the Oncology Clinical Team, this role focuses on training, validating, and continuously improving AI-driven oncology data extraction and trial-matching systems. This role collaborates closely with NLP, product, and operations teams to refine model outputs, build oncology data dictionaries, optimize abstraction logic, and ensure clinical accuracy across oncology workflows. Position Summary:This role works at the intersection of oncology and artificial intelligence. The role is responsible for evaluating and refining AI-generated outputs from electronic health records and clinical databases to ensure accuracy, consistency, and protocol alignment. Responsibilities include validating model-driven eligibility determinations, tuning oncology data dictionaries, improving abstraction logic, and supporting AI model performance across solid and hematologic malignancies.
Key Responsibilities
Review oncology patient cases for clinical trial eligibility using inclusion/exclusion criteria by validating and refining AI-assisted eligibility determinations.
Provide structured feedback to improve AI model performance on inclusion/exclusion logic.
Translate complex protocol requirements into structured, model-readable logic to enhance automated trial build and screening process.
Review and refine AI-extracted diagnostic, pathology, imaging, and treatment data for accuracy and protocol compliance.
Collaborate with NLP and product teams to tune oncology-specific data dictionaries and abstraction frameworks.
Define and improve abstraction rules to enhance automated clinical data structuring.
Serve as an oncology subject-matter expert to guide model optimization across solid and hematologic malignancies.
Identify systematic error patterns in AI outputs and propose workflow or model-level improvements.
Conduct second-level and quality-control reviews to benchmark AI performance.
Contribute to SOP development for AI-assisted abstraction workflows.
Qualification:
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Excellent written and verbal communication skills; able to collaborate with internal and external stakeholders in a fast-paced environment.
Sound judgment in abstracting medical data, with the ability to work independently and to escalate or seek input when appropriate.
High adaptability to changing assignments, project requirements, training schedules, and occasionally suboptimal tools or databases.
Proficient with Microsoft Office (Word, Excel, Outlook, PowerPoint); able to create, maintain, and share professional documents and reports.
Why Join us ?1. We are revolutionizing a unique industry that has the potential to impact and benefit patientsfrom all over the world - you can create impact at scale.2. We have had company - sponsored workations in Bali, Sri Lanka, and Manali and take pride in our hard-working yet super fun culture .3. We are working on a few of the most challenging problems in a highly regulated industry which provides you an opportunity to solve some of the most interesting things.4. You will get a chance to work with experts from multiple industries and the best in the industry Compensation. This role demands strong expertise in:
Histological classification of solid malignancies - mainly breast, lung, GI, GU and Gynecological cancers.
