Senior Principal Machine Learning Engineer

Cotiviti

Senior Principal Machine Learning Engineer driving AI/ML system architecture at Cotiviti. Leading data-driven initiatives to enhance payment accuracy and reduce clinical waste.

Posted 7/6/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $250,000 - $280,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in defining system architecture for AI/LLM-powered products, with a strong focus on building evaluation frameworks and driving data quality through clinician insights. Proven ability to lead end-to-end machine learning projects and establish reusable platform patterns.

Highest-signal resume keywords

PhD In Quantitative Discipline12+ Years Industry ExperienceDeep Expertise In LLM EvaluationProficiency In Python (PyTorch)Strong Experimentation Discipline

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning SystemsLarge-Scale ClassificationRanking SystemsA/B TestingCausal InferenceMetric DesignOpportunity Mining

Tools & Technologies

PythonPyTorchSQLPrestoTrinoSparkAirflow

Industry Keywords

AI ProductsLLM-Powered ProductsClinical DocumentationClaims ProcessingEvaluation Frameworks

Tech Stack

Tools & technologies

AirflowPythonPyTorchSparkSQL

About the role

Key responsibilities & impact

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.
Drive the data flywheel: convert expert clinician and auditor review decisions into high-quality labeled data.
Lead ranking and prioritization systems that surface the highest-value claims, audits, and care gaps for human review.
Establish reusable platform patterns — shared context stores, evaluation harnesses, feature pipelines.

Requirements

What you’ll need

PhD in a quantitative discipline such as Computer Science/Engineering, Statistics, Operations Research
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)

Benefits

Comp & perks

Medical, dental, and vision insurance coverage
Disability and life insurance coverage
401(k) savings plans
Paid family leave
9 paid holidays per year
17-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service