Lead Machine Learning Engineer

As a Lead Machine Learning Engineer, you'll join a growing team of world-class machine learning engineers and clinical experts to deploy machine learning models that help automate burdensome administrative clinical practices. You'll design, develop, and deploy advanced machine learning algorithms for retrieval, classification, and generative use cases, while building reliable and scalable production ML systems. You'll write and maintain reusable codebases for data preprocessing, model training, evaluation, and deployment, and provide software and machine learning best practice guidance to junior engineers. You'll work cross-functionally with product managers, clinicians, and technical leadership to align ML solutions with core objectives.

Responsibilities

• Design, develop and deploy advanced machine learning algorithms for retrieval, classification and generative use cases
• Develop reliable and scalable production machine learning systems
• Write and maintain reusable codebases to perform data preprocessing, model training, evaluation, and deployment
• Provide software and machine learning best practice guidance to junior engineers
• Work cross-functionally with diverse stakeholders, including product managers, clinicians, and technical leadership to align ML solutions with core objectives

Requirements

• Minimum 7+ years of experience in applied ML with a masters degree or 5+ years with a PhD, ideally in a senior or lead role in a large technology or healthcare company
• Clear understanding of model building, model maintenance and the measures that optimize models for production use
• Understand experimental design and can independently perform collection, measurement, and interpretation of results
• Expert in Python with experience in deep learning frameworks (e.g., PyTorch)
• Hands on experience building deep learning models (e.g., transformers) for NLP tasks
• MS or PhD required
• Experience with generative AI or Retrieval Augmented Generation (RAG)
• Experience working with unstructured healthcare data (e.g., clinical notes)
• Experience with OCR or image-based document understanding techniques
• Hands on experience using AWS tools such as SageMaker Studio