Optum
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.
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.
Primary 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, 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 Required Qualifications
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 Preferred Qualifications
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 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, 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 We offer a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution. The salary for this role will range from $110,200 to $188,800 annually based on full-time employment. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
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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, 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 Required Qualifications
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 Preferred Qualifications
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 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, 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 We offer a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution. The salary for this role will range from $110,200 to $188,800 annually based on full-time employment. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
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