Senior NLP Research Engineer

OceanAI

Remote · $120K – $155K · Full-time · Posted 6 months ago

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About the role

Our laboratory develops natural language processing systems for clinical documentation — specifically, the automated extraction of structured clinical information from free-text physician notes and discharge summaries. The clinical NLP domain presents challenges that distinguish it from general-purpose NLP: domain-specific terminology, highly variable writing styles across practitioners, implicit reasoning chains that must be made explicit, and a tolerance for error that is substantially lower than most commercial applications. We are seeking a senior research engineer who operates at the intersection of applied research and production engineering. You will be expected to read and critically evaluate current literature, propose experimental directions grounded in evidence, implement and evaluate those experiments rigorously, and translate validated improvements into our production pipeline. The role requires both the analytical discipline of a researcher and the engineering judgement of someone who has shipped and maintained systems that real clinicians depend on.

Fully remote.

Responsibilities

Design and lead experiments to improve entity extraction accuracy on clinical text
Evaluate and integrate recent NLP research into our production pipeline where validated
Maintain and improve our evaluation benchmark suite for clinical NLP tasks
Write technical reports on experimental outcomes for internal and external audiences
Mentor two junior NLP engineers and contribute to their research development

Requirements

5+ years of NLP engineering with a demonstrable research component — publications, conference presentations, or substantive research contributions
Deep familiarity with transformer architectures — you understand attention mechanisms, not just the Hugging Face API
Production experience fine-tuning large language models for domain-specific tasks
Strong Python engineering: modular, tested, documented code
Experience with clinical or biomedical NLP corpora (MIMIC, i2b2, or similar) is a significant advantage
Familiarity with evaluation frameworks specific to information extraction: span-level F1, entity-level metrics
LangChain or equivalent for orchestrating multi-step NLP pipelines