Sr Manager AI/ML Engineering

UnitedHealth Group•Eden Prairie, MN

19h•$148,900 - $255,300•Remote

About The Position

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care’s most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. Optum AI is UnitedHealth Group’s enterprise AI team. We are AI/ML scientists and engineers with deep expertise in AI/ML engineering for healthcare. We develop AI/ML solutions for the highest impact opportunities across UnitedHealth Group businesses including UnitedHealthcare, Optum Financial, Optum Health, Optum Insight, and Optum Rx. In addition to transforming the healthcare journey through responsible AI/ML innovation, our charter also includes developing and supporting an enterprise AI/ML development platform. As a Senior Manager of AI/ML Engineering, you will lead teams responsible for building and operating scalable machine learning platforms and production ML systems across the enterprise. You will drive the design and implementation of ML infrastructure, model lifecycle management systems, and MLOps platforms that enable reliable experimentation, deployment, monitoring, and governance of machine learning and generative AI models. This role requires solid technical leadership, deep experience in MLOps and cloud-based ML platforms, and the ability to collaborate closely with data science, engineering, and platform teams. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

8+ years of experience in machine learning engineering, MLOps, or AI platform engineering building production ML systems and scalable model pipelines
6+ years of experience programming in Python for ML systems with familiarity with frameworks such as PyTorch, TensorFlow, or scikit-learn
5+ years of experience working with ML lifecycle platforms such as MLflow, Kubeflow, SageMaker, Azure ML, or GCP Vertex AI
5+ years of experience building cloud-native ML platforms using Docker, Kubernetes, and distributed systems
5+ years of experience working with distributed data processing and orchestration tools such as Spark, Ray, Airflow, Dagster, or Prefect
1+ year of experience using AI-assisted development or 'vibe coding' tools such as Codex, Claude Code, Cursor, Windsurf, or similar tools
1+ year of experience in Healthcare
Must be authorized to work in the United States without the need for current or future employer-sponsored visa sponsorship (e.g., H-1B, TN, F-1/OPT, CPT, or other employment-based visa status)

Nice To Haves

Master’s degree in Computer Science, Engineering, Data Science, or related discipline
Experience building low-latency inference systems and online feature serving architectures
Experience implementing Responsible AI practices including bias detection and model explainability
Experience operating multi-cloud or hybrid ML platforms
Contributions to open-source ML or MLOps tooling

Responsibilities

Lead and scale AI/ML engineering teams responsible for building ML platforms, model pipelines, and scalable AI infrastructure
Architect enterprise ML and GenAI platforms supporting experimentation, model training, evaluation, deployment, monitoring, and lifecycle management
Productionize machine learning and generative AI models using batch and real-time inference architectures
Build and operate MLOps and LLMOps pipelines including CI/CT/CD workflows for model testing, validation, versioning, and promotion across environments
Develop scalable cloud-native ML infrastructure using Docker, Kubernetes, and cloud ML platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI
Implement model monitoring and lifecycle management systems to track drift, latency, bias, and data quality while enabling automated retraining
Ensure governance, security, and compliance of ML systems including lineage, auditability, reproducibility, and observability
Partner with data scientists, data engineers, and software engineers to define production ML standards and scalable AI solutions