Senior Principal Machine Learning Engineer

Accountabilities

• Provide technical leadership across the development of AI-powered products, driving the architecture, implementation, and continuous improvement of scalable machine learning systems.
• Drive the architecture, implementation, and continuous improvement of scalable machine learning systems that improve payment accuracy, risk adjustment, and quality outcomes.
• Create advanced evaluation frameworks, including LLM-as-a-Judge, offline metrics, and online experimentation, ensuring accuracy, reliability, and auditability.
• Develop data flywheel strategies by transforming expert feedback and review decisions into high-quality training data and model improvements.
• Explore and develop patient-level digital twin approaches to unify clinical data processing and improve healthcare insights.
• Lead ranking, prioritization, and classification systems that identify high-value opportunities for review and improve operational efficiency.
• Establish reusable ML platform patterns, including shared context stores, evaluation frameworks, and feature pipelines.
• Partner with engineering, product, clinical, and analytics teams to define objectives, success metrics, and production strategies.
• Mentor senior engineers and elevate standards around machine learning engineering, experimentation, and system design.
• Influence enterprise-wide AI/ML strategy and contribute thought leadership within the broader industry.
• Drive technical excellence through strong documentation, rigorous experimentation, and scalable engineering practices.

Requirements

• PhD in a quantitative discipline such as Computer Science, Engineering, Statistics, Operations Research, or a related field covering advanced statistics, machine learning, and AI.
• 12+ years of industry experience building and deploying production machine learning systems at scale.
• Deep expertise in two or more areas including LLM evaluation, retrieval-augmented generation (RAG), ranking systems, large-scale classification, or AI model optimization.
• Proven experience leading end-to-end ML initiatives from problem definition through production deployment and measurable business impact.
• Strong experimentation background, including A/B testing, causal inference, metric development, and opportunity analysis.
• Advanced proficiency in Python, PyTorch, SQL at scale (such as Presto, Trino, or Spark), and distributed data pipeline technologies such as Airflow.
• Experience building LLM evaluation pipelines, supervised fine-tuning workflows, embedding models, or reranking systems is highly valued.
• Familiarity with healthcare data, including claims, electronic health records, clinical coding systems, or healthcare analytics is preferred.
• Experience designing ML systems for regulated, auditable, or high-stakes environments such as healthcare, finance, or fraud detection.
• Knowledge of secure data practices and compliance frameworks for sensitive information, including HIPAA environments.
• Strong problem-solving abilities with the ability to independently analyze data, make decisions, and communicate complex technical concepts.
• Ability to provide a secure remote workspace with reliable high-speed internet connectivity.

Benefits

• Competitive base salary range of $250,000-$280,000 per year, with final compensation determined by experience, education, skills, certifications, and business needs.
• Eligibility for discretionary bonus opportunities.
• Comprehensive benefits package including medical, dental, vision, disability, and life insurance coverage.
• 401(k) savings plan to support long-term financial planning.
• Paid family leave and supportive benefits for personal and family needs.
• Paid holidays and 17-27 days of paid time off (PTO) depending on level and tenure.
• Fully remote work environment with virtual interview process.
• Opportunity to lead impactful AI initiatives and shape machine learning systems used in a high-value industry.
• Exposure to advanced AI technologies and complex real-world data challenges.
• Collaborative environment with opportunities for technical leadership and professional growth.