Lead AI or ML Engineer - Remote

UnitedHealth Group•San Diego, CA

9h•Remote

About The Position

Lead the autonomous medical coding enablement 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 be responsible for architecture decisions, code reviews, and coordinating across teams. 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
7+ years building and operating ML systems in production with a track record of shipped impact
5+ years experience in C# or Python
5+ years of Cloud experience
5+ years experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
5+ MLOps: CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
2+ years experience LLMOps: prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails

Nice To Haves

Domain experience in recommendations/ranking, time-series forecasting, anomaly detection, optimization, or reinforcement learning
Privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
Data engineering for ML: ETL/ELT, SQL, distributed processing (e.g., Spark), and feature pipelines
Experimentation: A/B testing design/analysis, guardrail metrics, basic causal inference
Proven excellent communication and product sense; able to scope ambiguous problems and align stakeholders

Responsibilities

Lead end-to-end ML projects: problem definition, data strategy, feature engineering, modeling, evaluation, deployment, and monitoring
Architect scalable training and inference systems with strong SLAs, observability, and cost controls
Establish experimentation rigor: offline evaluation, A/B testing, guardrails, power analysis, and causal insights
Drive MLOps excellence: CI/CD for ML, reproducible pipelines, model registry and governance, automated retraining, drift/quality monitoring
Collaborate with product and design to translate ambiguous goals into measurable ML problems; define success metrics and attribution
Mentor and unblock engineers; conduct design and code reviews; set patterns for reliability, documentation, and testing
Partner with data engineering on feature pipelines, data contracts, and online/offline parity; champion data quality
Communicate tradeoffs and results to technical and non-technical stakeholders; influence roadmap and prioritization
Optional focus areas depending on interest and business needs: LLM applications (RAG, fine-tuning, evaluation/guardrails), recommendations/ranking, anomaly detection, forecasting