Lead AI/ML Engineer - Remote

Requisition Number: 2350368
Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? 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.
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 strong 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

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:

• Bachelor of Science or higher 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 C# or Python
• 5+ years experience in Azure
• 5+ years experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
• 5+ MLOps: CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
• 2+ years experience LLMOps: prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails

Preferred Qualifications:

• Domain experience in recommendations/ranking, time-series forecasting, anomaly detection, optimization, or reinforcement learning
• Privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
• Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
• 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
• Experimentation: A/B testing design/analysis, guardrail metrics, basic causal inference

*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $112,700 to $193,200 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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.
UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment.
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