Senior Principal Machine Learning Engineer

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 from clinical 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.
• Establish reusable platform patterns — shared context stores, evaluation harnesses, feature pipelines — that compound value across product surfaces and lines of business.
• Partner across engineering, product, clinical, and analytics teams to align on success criteria, roadmap priorities, and production rollout.
• Mentor senior engineers and elevate organization-wide standards in ML craftsmanship, experimentation rigor, and system design.
• Sets company-wide standards . Acts as a thought leader beyond Cotiviti to elevate the reputation and visibility of Cotiviti in the industry.
• Influences the enterprise AI/ML strategy at an executive level.

Qualifications

• PhD in a quantitative discipline such as Computer Science/Engineering, Statistics, Operations Research covering Advanced Statistics, Machine learning and AI.
• 12+ years of industry experience building production ML systems at scale.
• Deep expertise in two or more of: LLM evaluation, retrieval-augmented generation (RAG), ranking, or large-scale classification.
• Proven track record leading end-to-end ML projects, from problem framing through production impact.
• Strong experimentation discipline: A/B testing, causal inference, metric design, and opportunity mining.
• Proficiency in Python ( PyTorch ), SQL at scale (Presto / Trino / Spark), and distributed pipeline tooling (Airflow).
• Demonstrated ability to drive cross-functional alignment across engineering, product, and analytics.
• Hands-on supervised fine-tuning of embedding or reranking models with measurable production gains.
• Experience with healthcare data (claims, electronic health records, or clinical coding such as ICD, CPT, or HCC).
• Background designing ML systems in regulated, auditable, or high-stakes domains (healthcare, finance, or fraud, waste, and abuse detection).
• Familiarity with building systems that handle sensitive data under frameworks such as HIPAA.
• Background building canonical data services or platform-level ML infrastructure adopted organization-wide.
• Applied mathematics, statistics, or quantitative PhD background.
• LLM ecosystem: RAG pipelines, LLM-as-a-Judge evaluation, prompt engineering, supervised fine-tuning.
• Cognitive/ Mental Requirements: Communicating with others to exchange information. Problem-solving and thinking critically. Completing tasks independently. Interpreting data. Making timely decisions in the context of a workflow.