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Sr Machine Learning Engineer (NLP)

CLARA analytics

CLARA analytics

Software Engineering
Posted on Friday, May 3, 2024

Senior Machine Learning Engineer (NLP)

About Us

Clara Analytics' mission is to give insurance claims managers the power to improve outcomes dramatically, save millions of dollars, and help thousands of people recover from injuries, recoup damages, and get back on their feet faster after suffering a loss. The opportunities to develop high-value AI and machine learning applications in the insurance industry are nearly unbounded - the sector has only just begun to realize the benefits of adopting these technologies. We are looking for high performers with an entrepreneurial spirit who welcome the challenges and opportunities of thinking big in uncharted territory and are driven to solve challenging problems and develop cutting-edge applications that have a significant impact.

Job Description

We are seeking a highly skilled and experienced Senior Machine Learning Engineer with a specialization in Natural Language Processing (NLP) to join our team. The ideal candidate will have a strong background in machine learning, deep learning, and NLP techniques including Large Language Models (LLMs), with a proven track record of developing and deploying state- of-the-art NLP models to a production environment. You will utilize your expertise to extract information from structured, semi-structured, and unstructured text data that may originate from forms, invoices, medical records, legal documentation, etc.


  • 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.

Minimum Qualifications

  • 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.

Preferred Qualifications

  • PhD in quantitative discipline as described above.
  • 2+ years of relevant work experience in the insurance or medical industries, including development and implementation of production quality machine learning applications.
  • Experience coupling language models with other tools and technologies (knowledge graphs, domain-specific ontologies, etc.) to overcome their limitations.