Sr Manager AI/ML Engineering - Hybrid In MN Or DC, Remote Elsewhere

Transform Healthcare Through Ai Innovation At Optum

Optum is a global organization delivering care, powered by data and technology, to help millions of people live healthier lives. At Optum.ai, we are not just witnessing the AI transformation in healthcare-we are leading it. Our mission is clear: to simplify healthcare with AI, turning insight into action at a scale few organizations in the world can match.

As part of the Optum.ai team, you'll work at the intersection of cutting-edge artificial intelligence and real-world healthcare impact. From reducing administrative burden for providers to anticipating patient needs and improving access to quality care, your work will help solve some of healthcare's most complex challenges-and directly improve health outcomes for millions of people.

You'll collaborate with world-class talent across data science, engineering, product, and healthcare domains, backed by the reach and stability of Optum and UnitedHealth Group. Here, responsible innovation matters. So do comprehensive benefits, meaningful career growth, and the opportunity to make a tangible difference-advancing health equity and creating a simpler, more connected healthcare experience for everyone.

This is more than a job. It's a chance to shape the future of healthcare through the transformative power of AI. 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.

Senior Manager Of Ai/Ml Engineering

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 strong 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.

Primary 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

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:

• 8+ years of experience in machine learning engineering, MLOps, or AI platform engineering building production ML systems and scalable model pipelines
• 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
• 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 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

Preferred Qualifications:

• 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