Senior AI/ML Engineer

About the job

Responsibilities

• Build predictive models on clinical data at scale, including risk stratification, expected utilization prediction, and care gap detection.
• Own the full ML lifecycle, from problem framing and feature engineering to training, evaluation, deployment, and monitoring.
• Transform unstructured clinical data (e.g., PDFs, images, doctor notes) into structured, usable records.
• Establish ML infrastructure, including training and inference pipelines, experiment tracking, model versioning, and evaluation systems.
• Collaborate with founders, engineers, and customers to identify and prioritize high-leverage ML problems.
• Build models that predict expected hospitalizations for new patients.
• Develop classifiers to categorize billions of clinical documents with incomplete metadata.

Requirements

• Deep ML fundamentals, covering classical methods through deep learning.
• Experience shipping models to production, including evaluation, monitoring, retraining, and post-launch issue management.
• Strong engineering skills, including service deployment, Infrastructure as Code (IaC), instrumentation, and end-to-end ownership.
• Proficiency in Python; comfortable with TypeScript, AWS, and SQL.
• Pragmatic approach to LLMs, understanding when to use them and when smaller, more efficient models are appropriate.
• Experience working with messy, real-world data (sparse, inconsistent, high-dimensional).
• Entrepreneurial mindset with a strong work ethic.
• Ability to question project scope and identify faster, efficient solutions.
• Hacker mentality with good judgment on when to break rules.
• 7+ years of experience building ML systems.