Senior Manager AI/ML Engineering - Remote

UnitedHealth Group•San Diego, CA

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 in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together. We are seeking a highly experienced and hands-on Senior Manager of AI/ML Engineering to lead the ML engineering strategy and execution behind our cutting edge autonomous medical coding solutions. In this role, you will guide a team of machine learning engineers and collaborate closely with data scientists, software engineers, product managers, clinical experts, and compliance partners to build scalable, reliable, and secure AI systems for processing millions of medical charts every day. You will be responsible for translating emerging advances in machine learning and LLMs into production-ready capabilities that improve coding accuracy, efficiency, auditability, and clinical workflow integration. This is a high-impact leadership role in a fast-paced environment where you will help shape both the technical roadmap and the engineering culture of the organization. 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

6+ years of technical experience in AI/ML engineering, machine learning development, or applied AI solution delivery
2+ years of direct people management or technical leadership experience leading AI/ML engineering teams
Proven experience in architecting and deploying production-grade machine learning pipelines, LLM-powered applications, and MLOps platforms handling large-scale data processing
Hands-on experience in establishing evaluation frameworks, model monitoring, continuous integration/deployment (CI/CD), and responsible AI standards for production models
Demonstrated experience deploying Large Language Models (LLMs), retrieval-augmented generation (RAG), and prompt engineering in enterprise production environments
Solid technical proficiency in Python and deep learning/NLP frameworks (e.g., PyTorch, TensorFlow, Hugging Face)

Nice To Haves

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field (or 4+ additional years of equivalent technical experience)
Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline
Experience in healthcare, clinical NLP, medical chart processing, medical coding automation (ICD-10, CPT), or HIPAA-compliant environments
Experience with containerization, orchestration, and infrastructure-as-code tools (e.g., Docker, Kubernetes, Terraform)

Responsibilities

Lead, mentor, and grow a high-performing team of AI/ML engineers focused on medical coding automation
Define and execute the engineering strategy for machine learning and LLM-powered products at production scale
Partner with data science and clinical teams to develop solutions for chart understanding, clinical concept extraction, code assignment, documentation analysis, and coding validation
Drive the architecture of scalable inference and data-processing systems capable of handling millions of medical charts daily
Ensure that AI systems meet stringent requirements for accuracy, reliability, latency, cost efficiency, privacy, security, and explainability
Collaborate with software engineering leaders to integrate ML capabilities into robust SaaS products and customer workflows
Partner with product and business stakeholders to prioritize initiatives, define success metrics, and translate customer needs into technical solutions
Work with legal, compliance, security, and clinical stakeholders to support HIPAA-compliant, responsible, and auditable use of AI in healthcare
Develop evaluation frameworks and quality metrics for coding accuracy, clinical validity, hallucination prevention, confidence estimation, and human review prioritization