Sr Manager AI/ML Engineering - Remote Nationwide or Hybrid in MN/DC

UnitedHealth Group•Minnetonka, MN

1d•$148,900 - $255,300•Hybrid

About The Position

As a Senior Manager, AI/ML Engineering within our UHC Operations and Experience division at Optum Technology, you will lead a highly skilled team of engineers driving next-generation machine learning and artificial intelligence capabilities. Our team’s mission is to (Input Required: Insert Team Mission/Vision). In this role, you will lead the architecture, design, and deployment of complex AI/ML software in production environments, using technologies such as natural language processing (NLP), natural language understanding (NLU), and deep learning. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. This position follows a hybrid schedule with four in-office days per week.

Requirements

Bachelor’s degree or equivalent experience
7+ years of experience in software engineering, with at least 4+ years of specialized experience in AI, machine learning, deep learning, or data science
5+ years of experience coding in Python for tool-building, model training, and data analysis
4+ years of experience implementing machine learning models in production environments using frameworks such as TensorFlow, PyTorch, or Scikit-Learn
3+ years of experience working with large-scale data systems and distributed computing frameworks (e.g., Spark, Hadoop, Kafka, or Databricks)
3+ years of experience building and deploying software solutions within cloud-based platforms, specifically Azure
3+ years of experience applying experimental methodologies, statistics, optimization, and probability theory to solve complex technical problems
3+ years of experience directly leading, mentoring, or managing technical teams of software or AI/ML engineers

Nice To Haves

Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative technical field
Experience with NLP/NLU (natural language processing/understanding), semantic understanding, intent classification, computer vision, deep learning, and automatic speech recognition (ASR)
Experience developing applications with LLM integration using advanced frameworks such as LangChain and LangGraph
Experience in the healthcare, clinical data, or operations domains
Solid knowledge of MLOps best practices, model tracking (e.g., MLflow, Kubeflow), and cloud-based AI deployment (e.g., AWS, Azure, GCP)
Proven excellent communication skills, with a demonstrated ability to explain complex statistical and analytical concepts to non-technical business partners and leadership

Responsibilities

Lead, mentor, and grow a team of high-performing AI/ML Engineers focused on researching, developing, and deploying advanced machine learning models and algorithms in production environments
Oversee the design, development, and deployment of complex AI-powered solutions addressing business challenges, ensuring collaboration across research, engineering, and product teams to translate cutting-edge AI advancements into scalable, reusable production-ready capabilities
Evaluate emerging AI trends, scientific research, and advanced methodologies to inform team solution design, architectural direction, and strategic innovation
Promote the leverage of enterprise-approved AI tools within the team to streamline engineering workflows, automate routine tasks, and drive continuous improvement
Direct the development of scalable code and software systems that integrate advanced artificial intelligence capabilities (including NLP, NLU, deep learning, computer vision, and automatic speech recognition)
Partner with leadership, product owners, and internal stakeholders to translate complex analytics results, business needs, and cutting-edge research findings into practical, production-ready AI solutions
Ensure rigorous experimental methodologies, advanced statistical analysis, probability theory, and optimization techniques are applied systematically across all model developments
Oversee ML operations (MLOps) workflows, ensuring seamless model monitoring, validation, version control, and continuous integration/deployment
Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the complete model research and development lifecycle