Senior AI/ML Engineer - Remote

UnitedHealth Group•San Diego, CA

1d•$120,100 - $214,500•Remote

About The Position

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.

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
Open-source contributions, publications, or patents experience
MLOps expertise including CI/CD for ML, containers, Kubernetes, model registries, reproducibility, and model monitoring
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)
Proven solid ML/statistics fundamentals: supervised learning, evaluation methodology, feature engineering, bias/variance tradeoffs; deep learning or gradient boosting experience

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