Judi Health
Senior Machine Learning Engineer II
$150K–$226K
Charlotte, North Carolina, United States +5 · Senior · Full-time · Posted Aug 5, 2026
- Location
- Charlotte, North Carolina, United States
Denver, Colorado, United States
New York, New York, United States
Charlotte, North Carolina, United States
Denver, Colorado, United States
New York, New York, United States - Experience
- Minimum 6+ years of industry experience; no alternatives stated.
- Education
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
About the job
Designs, builds, and deploys machine learning and AI systems for healthcare benefit products, including document understanding, search, Q&A, and virtual agents.
What you'll do
- Productionize ML solutions and develop Python backend services and APIs for AI applications.
- Design multi-agent orchestration and scale retrieval/search infrastructure for RAG systems.
- Evaluate and validate AI systems using telemetry, A/B testing, automated testing, and statistical methods; ensure healthcare data security and HIPAA compliance.
What they ask for
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Minimum 6+ years of industry experience focused on ML solution development and production deployment.
- Strong proficiency in Python; familiarity with ML libraries such as PyTorch.
- Experience designing evaluation frameworks for non-deterministic/agentic AI systems.
Nice to have
- Familiarity with cloud platforms such as AWS or Azure and hands-on experience with containerized deployments.
- Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma, and prompt development or fine-tuning.
Pay
$180,400 — $225,500 USD (base salary); $165,600 — $207,000 USD (base salary); $150,400 — $188,000 USD (base salary)
Tools
Python, PyTorch, AWS, Azure, OpenAI GPT, Anthropic Claude, Google Gemma, RAG
AI focus
Builds ML, GenAI, and agentic AI systems for document understanding, NLP, search and Q&A, virtual agents, and multimodal analysis, including RAG infrastructure and evaluation.
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