Natural Language Processing (NLP) Engineer (Senior)

At Diagnoss, we develop artificial intelligence to support clinicians and medical billing teams. Our machine learning models learn from the clinician note to deliver highly accurate predictions for tasks in healthcare. Founded by machine learning researchers and healthcare professionals with ties to UC Berkeley and in partnership with the nations’ largest medical organizations, we are looking to expand our team and seeking an exceptional, self-starter Senior NLP Engineer who is excited to change the future of healthcare with the power of machine learning.

In this role, you will:

Design, implement, and train state-of-the-art machine learning/deep learning architectures
Isolate pertinent healthcare prediction tasks and formulate experimental plans
Establish evaluation metrics, analyze performance, brainstorm future steps, perform exploratory data analysis and perform data visualization
Train and evaluate machine learning (ML) and NLP models for multi-label classification and regression problems
Improve runtime, scalability, and reliability of ML systems
Develop and enhance data platform and infrastructure to intake, clean, and structure multi-modal healthcare data
Design data acquisition and data labeling systems
Work closely with engineering team members to translate best-performing techniques into production

Minimum qualifications:

4+ years of industry experience in ML
Track record of productionizing ML models
Track record of projects in supervised learning across multiple data domains that show extensive knowledge and ability to design models/experiments from scratch
Strong understanding of machine learning and deep learning fundamentals
Fluency in Python and ML/NLP libraries
Experience with databases and data structuring/warehousing
Experience with API development and integration
Excellent communication and ability to estimate accurate project timelines
Ability to read and replicate ML models described in academic papers with no supervision

Preferred qualifications:

Experience in NLP and ML on healthcare datasets
Previous experience integrating with electronic health records
Authored publications at leading AI/ML conferences (NeurIPS, ICLR, ICML, etc)

This is an opportunity to steer direction of early product development that requires strong technical foresight, self-directedness, and willingness to take on ownership. We view this as a leadership role.

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