Medical NLP Annotation - Oncology Adverse Event Detection

We are LUMIQO, an early-stage healthtech startup building an AI-powered clinical research assistant for adverse event detection, CTCAE grading, and structured AE log generation for oncology and hematology clinical trial sites. We are looking for a detail-oriented freelancer with a clinical or life sciences background to annotate synthetic clinical notes for our NLP pipeline. This is not general data labeling - it requires familiarity with oncology terminology, clinical documentation, and ideally some understanding of how adverse events are documented and graded in cancer care. What you will be doing: Reviewing synthetically generated oncology clinical notes designed to mimic real trial documentation Identifying and highlighting adverse events from unstructured clinical text Assigning CTCAE v6 severity grades (1-5) to each detected adverse event Flagging negations and hedged language - e.g. "no fever" vs "patient reports fever" Marking onset and stop dates where documented Linking source text evidence to each identified AE Flagging seriousness indicators where present Following a detailed annotation guide our team will provide Important notes: All data is synthetic - no real patient information is involved You will receive a detailed annotation guide before starting Initial batch will be small to assess quality and fit before scaling We are looking for someone who takes the work seriously, asks questions when uncertain, and flags ambiguity rather than guessing