Lead AI/ML Engineer

UnitedHealth Group • San Diego, CA

147d • $110,200 - $188,800 • Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. Lead the autonomous medical coding enablement in a SaaS platform by integrating machine learning and LLMs. Working closely with data scientists and software engineers through data extraction, research, training, and deployment to create a scalable production solution that can handle millions of medical charts daily. You will be responsible for architecture decisions, code reviews, and coordinating across teams. You will work with cutting edge models, LLM, software, and tools in a fast paced environment. You'll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Requirements

• BS/MS/PhD in Computer Science, Engineering, Statistics, or related field, or 4+ years of equivalent practical experience
• 7+ years building and operating ML systems in production with a track record of shipped impact
• 5+ years experience in Azure

Nice To Haves

• Domain experience in recommendations/ranking, time-series forecasting, anomaly detection, optimization, or reinforcement learning
• Solid software engineering fundamentals: production-grade C#/Python, testing, performance profiling, and code reviews
• Solid ML/statistics: supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
• Privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
• Excellent communication and product sense; able to scope ambiguous problems and align stakeholders
• Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
• Data engineering for ML: ETL/ELT, SQL, distributed processing (e.g., Spark), and feature pipelines
• LLMOps: prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails
• MLOps expertise: CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
• Experimentation: A/B testing design/analysis, guardrail metrics, basic causal inference

Responsibilities

• Lead end-to-end ML projects: problem definition, data strategy, feature engineering, modeling, evaluation, deployment, and monitoring
• Architect scalable training and inference systems with solid SLAs, observability, and cost controls
• Establish experimentation rigor: offline evaluation, A/B testing, guardrails, power analysis, and causal insights
• Drive MLOps excellence: CI/CD for ML, reproducible pipelines, model registry and governance, automated retraining, drift/quality monitoring
• Collaborate with product and design to translate ambiguous goals into measurable ML problems; define success metrics and attribution
• Mentor and unblock engineers; conduct design and code reviews; set patterns for reliability, documentation, and testing
• Partner with data engineering on feature pipelines, data contracts, and online/offline parity; champion data quality
• Communicate tradeoffs and results to technical and non-technical stakeholders; influence roadmap and prioritization
• Optional focus areas depending on interest and business needs: LLM applications (RAG, fine-tuning, evaluation/guardrails), recommendations/ranking, anomaly detection, forecasting
• Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows and decision-making

Benefits

• Comprehensive benefits package
• Incentive and recognition programs
• Equity stock purchase
• 401k contribution

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Industry: Insurance Carriers and Related Activities
• Education Level: Master's degree