Lead RL Decision Engineer — Healthcare Optimization

Humana Inc; Pierre (SD); Remote; USD 129,000 - 178,000; Full time

Job summary

Humana Inc. is seeking a Decision Intelligence Engineer to design, train, and improve reinforcement learning policies for the Next Best Action platform, guiding decisions for millions of members.

You will own the RL lifecycle from feature engineering to production deployment, balancing clinical eligibility, rewards, and real-world impact. The role is hands-on and research-oriented, requiring collaboration with data and platform engineers to ensure scalable, auditable, and explainable decisioning

Qualifications

• 8+ years of software engineering building large-scale production systems.
• 3+ years hands-on RL or deep learning systems in production.
• Proficiency in Python 3.x and ML frameworks (PyTorch or TensorFlow).
• Experience with distributed RL training and production policy gradients.
• Experience with Databricks, PySpark, Delta Lake, and MLflow.
• Ability to diagnose RL-specific failure modes and ensure explainability.

Responsibilities

• Design, implement, and evaluate RL algorithms for healthcare decisioning.
• Define and maintain member state representation and action space.
• Build training pipelines with Databricks and Ray RLlib; monitor via MLflow.
• Collaborate with data engineers to ensure reproducible inputs and governance.
• Ensure production safety and compliance with clinical eligibility rules.

Skills

RL implementation

Python

PyTorch or TensorFlow

Ray RLlib

Databricks / PySpark

MLflow

Distributed training

Model evaluation

Education

Bachelor's degree in computer science or related field

Tools

Databricks

PySpark

Delta Lake

MLflow

Job description

Humana Inc. is seeking a Decision Intelligence Engineer to design, train, and improve reinforcement learning policies for the Next Best Action platform, guiding decisions for millions of members.

You will own the RL lifecycle from feature engineering to production deployment, balancing clinical eligibility, rewards, and real-world impact. The role is hands-on and research-oriented, requiring collaboration with data and platform engineers to ensure scalable, auditable, and explainable decisioning