Research Engineer, Applied ML

ABOUT THE ROLE

We are looking for a Research Engineer to build applied NLP and LLM systems for healthcare and oncology workflows. You will work on real-world clinical text, information extraction, patient-trial matching, summarization, RWE abstraction, and structured outputs from clinical documents.

This role is ideal for someone who enjoys hands-on model development, experimentation, evaluation, and building practical ML systems that can eventually move toward production.

WHAT YOU WILL DO

Build prototypes and production-oriented components for clinical information extraction and LLM workflows.

Implement prompts, RAG pipelines, fine-tuning experiments, extraction logic, evaluation scripts, and error-analysis tools.

Work with senior engineers to improve model quality, robustness, and reliability.

Analyze clinical-text model failures and propose meaningful improvements.

Collaborate with Clinical AI Data Specialists to understand labeling guidelines, data quality issues, and clinical edge cases.

Collaborate with ML Evaluation Engineers to run benchmark and regression tests.

Write clean, tested, maintainable Python code and contribute to internal tools, data processing scripts, and annotation/evaluation workflows.

WHAT WE EXPECT

1.5–4 years of hands-on experience in ML, NLP, LLMs, or data-intensive software engineering.

Strong Python programming skills and willingness to work with messy real-world data.

Familiarity with PyTorch, HuggingFace, embeddings, transformers, RAG, prompt engineering, and basic fine-tuning.

Good understanding of precision/recall/F1, train/validation/test splits, overfitting, error analysis, and experiment tracking.

Ability to learn clinical domain concepts and ask clarifying questions about labels and data.

Strong ownership of assigned modules and tasks.

NICE TO HAVE

Clinical NLP, biomedical text, healthcare documents, or information extraction experience.

Experience with LangChain/LlamaIndex, vector databases, spaCy, OCR/document processing, annotation tools, or structured LLM outputs.

Exposure to Docker, APIs, ML deployment, MLflow/W&B, or data pipelines.

SUCCESS IN 6 MONTHS

Independently owns modules or bounded workstreams.

Writes reliable code and experiments with clear documentation.

Can analyze model errors and suggest meaningful improvements.

Collaborates well with clinical data, evaluation, and MLOps functions.

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.

Why Join Triomics

Impact 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 & Benefits

Lunch 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.