Senior Machine Learning Engineer - NLP

2 days ago

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CLARA Analytics

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Proven AI Platform for Casualty Insurance Claims

workers'​ compensation • occupational injuries • insurtech • data science • predictive analytics

51 - 200

Description

• Develop and deploy cutting-edge NLP models for extracting structured information from unstructured medical text data, including clinical notes and diagnostic reports. • Conduct data preprocessing, feature engineering, and model evaluation to optimize NLP model performance on diverse medical datasets. • Design data annotation requirements for a third-party. • Monitor progress and quality of annotations received, providing feedback and guidance as necessary. • Develop and deploy models focused on various NLP applications such as: topic modeling, named entity recognition, text classification, summarization, question answering, content verification, and more. • Stay abreast of the latest research and advancements in NLP and incorporate relevant techniques and methodologies into our NLP solutions. • Work closely with our product and engineering teams to ensure clear functional requirements that enable us to design and develop new applications and features. • Conduct code and model reviews of your peers, providing actionable feedback to ensure a high standard of quality.

Requirements

• MS degree in a quantitative discipline (e.g., machine learning, computer science, mathematics, physics). • 5+ years of experience developing machine learning models, including 2+ years of model deployment experience. • 3+ years of practical experience in natural language processing, plus a strong academic background. • Hands on experience with text preprocessing, named entity recognition and entity linking, topic modeling, document classification, summarization (extractive and abstractive), and document-based question answering. • Solid understanding of NLP fundamentals including word embeddings, sequence-to-sequence models, attention mechanisms, and transformer architectures. • Ability to assess the pros and cons of different NLP methods and algorithms, break problems down into standard tasks and prototype quickly. • Experience with building and deploying NLP models in production environments, including knowledge of containerization technologies (Docker) and cloud platforms (AWS). • Experience with large language models and their associated tools/platforms/frameworks (LangChain, HuggingFace, PySpark, etc.) for use in querying large documents, entity extraction, and summarization. • Demonstrated ability to communicate complex quantitative concepts effectively to audiences of varying technical proficiency.