Senior AI/ML Engineer - Remote

UnitedHealth Group•San Diego, CA

3d•Remote

About The Position

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. This role will enable autonomous medical coding 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 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. 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. 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.

Requirements

Bachelor of Science or higher in Computer Science, Engineering, Statistics, or related field, or 4+ years of equivalent practical experience
5+ years of industry experience building and operating ML systems in production (or equivalent depth), with a track record of shipped impact
3+ years of experience in C# or Python
3+ years of Azure experience
3+ years of experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
3+ years of MLOps expertise including CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
1+ years of experience with LLMOps including prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails

Nice To Haves

Domain experience in recommendations, ranking, time-series forecasting, optimization, or reinforcement learning
Demonstrated excellent communication and product sense; able to translate business needs into technical plans and explain tradeoffs to non-ML stakeholders
Proven privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
Open-source contributions, publications, or patents experience
Proven solid ML/statistics fundamentals: supervised learning, evaluation methodology, feature engineering, bias/variance tradeoffs; deep learning or gradient boosting experience
MLOps expertise including CI/CD for ML, containers, Kubernetes, model registries, reproducibility, and model monitoring

Responsibilities

Ship production ML systems end-to-end: problem framing, data discovery, feature engineering, training, evaluation, deployment, monitoring, and iteration
Design robust ML system architectures with low-latency inference and high availability
Build and maintain reliable data and model pipelines using modern MLOps practices (CI/CD for ML, model registries, experiment tracking, automated retraining)
Contribute to technical scoping and break down complex initiatives into executable roadmaps; drive execution across cross-functional partners
Establish evaluation strategies: metrics, simulation, counterfactuals, and A/B tests; quantify impact and ensure statistical rigor
Implement model observability and governance: drift detection, performance monitoring, fairness/bias assessments, and model documentation
Collaborate closely with product, design, data, and platform teams to translate product goals into ML opportunities and measurable outcomes