Lead RL & Decision Intelligence Engineer

Humana Inc; Honolulu (HI); Hybrid; USD 129,000 - 178,000; Full time

Job summary

Humana Inc. in the United States invites a senior Decision Intelligence Engineer to design, train, and improve reinforcement learning policies behind the Next Best Action platform for millions of members.

You will own end-to-end RL development, from feature design and reward shaping to distributed training with Ray RLlib, Databricks pipelines, and MLflow. This hands-on role emphasizes production-readiness, governance, and explainability, with collaboration across data, platform, and Rules Engine

Qualifications

• Bachelor's degree in computer science or related field.
• 8+ years of software engineering experience building and operating large-scale production systems.
• 3+ years implementing reinforcement learning or deep learning systems in production.
• Proficiency in Python 3.x; experience with PyTorch or TensorFlow for policy network implementation.
• Experience with Ray RLlib for distributed RL training at scale, Databricks and MLflow.

Responsibilities

• Design, implement, and evaluate RL algorithms for healthcare decisioning.
• Define member state representation and action space for onboarding data sources.
• Collaborate with data and platform engineers to ensure training inputs and reward signals are correct.
• Instrument training runs with MLflow tracking and metrics.
• Ensure model governance, explainability, and compliance in production.

Skills

Reinforcement learning

Python

Distributed training

Model evaluation

Production ML systems

Education

Bachelor's degree in computer science or related field

Tools

PyTorch

Ray RLlib

Databricks

MLflow

PySpark

Job description

Humana Inc. in the United States invites a senior Decision Intelligence Engineer to design, train, and improve reinforcement learning policies behind the Next Best Action platform for millions of members.

You will own end-to-end RL development, from feature design and reward shaping to distributed training with Ray RLlib, Databricks pipelines, and MLflow. This hands-on role emphasizes production-readiness, governance, and explainability, with collaboration across data, platform, and Rules Engine