Senior Principal Machine Learning Engineer

Overview

We are looking for a Senior Principal Machine Learning Engineer to lead the design and delivery of end-to-end ML/AI systems that turn vast volumes of claims, clinical, and member data into measurable performance and reduced waste. You will define technical strategy, drive cross-functional alignment, and own systems that directly shape payment accuracy, risk adjustment, and quality outcomes for the payers we serve. This role sits at the intersection of applied research and production engineering, translating ambiguous, high-stakes problems into scalable, auditable ML solutions.

The ideal candidate has operated at large scope across multiple teams and product surfaces — not just shipped models, but defined the problem, built the evaluation infrastructure, created the data flywheel, and drove measurable business outcomes. They think in systems, write crisp design docs, bring intellectual honesty to experimentation, and treat auditability and precision as first-class requirements rather than afterthoughts. They raise the level of the engineers around them. Responsibilities: * Define system architecture for AI/LLM-powered products end to end over claims, medical records, and clinical documentation. * Build and own evaluation frameworks (LLM-as-a-Judge, offline metrics, online experiments) aligned to accuracy, auditability, and clinical and regulatory risk — because outputs inform payment and compliance decisions. * Drive the data flywheel: convert expert clinician and auditor review decisions into high-quality labeled data, and close the loop with fine-tuning of models to lift detection precision. * Explore building patient-level digital twins fromclinical charts for unified processing layer and data presentation across payment, risk and quality. * Lead ranking and prioritization systems that surface the highest-value claims, audits, and care gaps for human review, improving both reviewer efficiency and financial impact....