Senior ML Research Engineer

About TriomicsTriomics 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.GROWTH PATHThis is an individual contributor role with strong ownership expectations. High performers may be considered for workstream lead or functional lead responsibilities after approximately 12 months (6 months for exceptional candidates), based on demonstrated ownership, delivery, technical judgment, mentoring, cross-functional influence, and ability to reduce dependency on the Director of ML.ABOUT THE ROLEWe are looking for a Senior Research Engineer to build applied ML and LLM systems for oncology-focused clinical information extraction, real-world evidence workflows, patient-trial matching, registry/QI abstraction, and clinical data products. This is a hands-on role for someone who can move between research, prototyping, evaluation, and production collaboration.You will work on extracting structured, evidence-backed information from complex longitudinal clinical records such as notes, pathology reports, molecular reports, imaging reports, scanned documents, and other oncology data sources. You will partner closely with Research Engineers, Clinical AI Data Specialists, ML Evaluation Engineers, MLOps/Data Engineers, and clinical experts.WHAT YOU WILL DODesign and implement LLM/NLP-based information extraction systems for oncology and RWE use cases.Build and improve pipelines for entity extraction, normalization, summarization, patient-trial matching, eligibility reasoning, and structured clinical abstraction.Explore prompting, fine-tuning, RAG, agentic workflows, constrained structured outputs, weak supervision, active learning, and hybrid ML/rule-based systems.Define experiments, baselines, ablations, error-analysis plans, and practical evaluation approaches.Collaborate with Clinical AI Data Specialists to convert clinical requirements into ML-feasible extraction tasks and labeling guidelines.Collaborate with ML Evaluation Engineers to validate model quality, regression behavior, segment-level performance, and release readiness.Collaborate with MLOps/Data Engineering to move reliable models and workflows into production.Write technical design documents, review code, mentor junior engineers/interns, and drive bounded workstreams independently.Over time, demonstrate readiness to own larger technical workstreams end to end.WHAT WE EXPECT4–7+ years of hands-on experience in ML/NLP/LLM engineering, preferably with applied production or near-production systems.Strong Python and PyTorch experience; ability to write clean, reusable, testable code beyond notebooks.Strong understanding of LLMs, transformers, embeddings, RAG, fine-tuning, prompt engineering, evaluation, and structured output generation.Ability to reason about model errors, data quality, annotation ambiguity, hidden test sets, and clinical edge cases.Experience building reusable ML pipelines and experimental frameworks.Strong engineering habits: versioning, tests, documentation, reproducibility, and peer review.Ability to mentor junior engineers and communicate technical tradeoffs clearly.NICE TO HAVEClinical NLP, biomedical NLP, healthcare data, oncology data, RWE, EHR data, pathology, biomarkers, cancer registry abstraction, or clinical trial eligibility experience.Experience with LoRA/SFT/DPO, synthetic data generation, weak supervision, active learning, human-in-the-loop systems, or preference optimization.Experience with vLLM, Ray, Triton, vector databases, LangChain/LlamaIndex, MLflow/W&B, or similar tooling.Experience writing technical design docs and leading technical discussions.SUCCESS IN 6 MONTHSOwns at least one major ML workstream with limited day-to-day supervision.Produces reliable design docs, experiment plans, and release-ready model improvements.Mentors junior REs or interns effectively.Reduces dependency on the Director of ML for implementation details.Demonstrates readiness for workstream lead or technical lead responsibilities.Why Join TriomicsImpact at scale. The systems your teams build directly power AI workflows that accelerate cancer research and improve patient outcomes.Cutting-edge problems. Hard, data-intensive systems at the intersection of AI, healthcare, and scale - in a highly regulated industry where reliability is non-negotiable.World-class team. Work alongside top talent across AI, engineering, and product, with best-in-industry compensation.Culture that ships. Fast-paced, ownership-driven, with company-sponsored workations.Perks & BenefitsLunch provided at the office - one less daily decision.Flexible working hours - we care about output, not clock-ins.Comprehensive health insurance for you and your family.Zomato meal benefits for early starts and late nights.Remote Wednesdays