Conducts machine learning research in medical NLP for conversation summarization, evidence extraction, and outcome prediction. Publishes at top AI conferences, deploys models to production, and requires MS/PhD plus strong PyTorch/TensorFlow experience.
205k – 300k/yr
HybridAI Research
About the role
What You'll Do
Advance the state of the art in medical NLP, in areas including conversation summarization, evidence extraction, outcome prediction, evaluation techniques, and experimentation.
Actively contribute to the wider research community by sharing and publishing original research
Help to define important problems, identify appropriate baselines, develop state-of-the-art methods, and ship them into production.
Dial deeply into real-time feedback from clinicians to guide further refinements and innovations
Be results-oriented in the face of ambiguous problems and uncertain outcomes
What You'll Bring
Strong research background, as demonstrated through papers and a graduate degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or equivalent experience.
High-impact publications at peer-reviewed AI conferences (e.g. ACL, COLM, EACL, NAACL, NeurIPS, ICML, ICLR, Interspeech, ICASSP, SLT, MLHC, ML4H).
Significant real-world impact, as demonstrated through open source contributions and deployed technology.
Strong programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production.
Experience with deep learning libraries (e.g. PyTorch, Jax, Tensorflow) and platforms, multi-GPU training, and statistical analyses of observational and experimental data.
Skills
PyTorchJAXTensorFlowNatural Language ProcessingMachine LearningDeep LearningMulti-Gpu TrainingStatistical AnalysisFoundation Models
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