Lead RL Engineer - Decision Intelligence

Humana Inc; Cheyenne (WY); Hybrid; USD 129,000 - 178,000; Full time

Job summary

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

You will own the full RL lifecycle—from feature engineering to distributed training, evaluation, and production deployment—ensuring decisions are explainable and auditable. The role combines healthcare outcomes with decision engineering, requiring collaboration with data, platform, and governance teams to

Qualifications

• Bachelor's degree in computer science or related field.
• 8+ years of software engineering experience building large-scale systems.
• 3+ years of hands-on experience with reinforcement learning in production.
• Proficiency in Python and ML frameworks (PyTorch or TensorFlow).
• Experience with Ray RLlib for distributed RL training at scale.
• Experience with Databricks, PySpark, and Delta Lake for ML pipelines.

Responsibilities

• Design, implement, and evaluate RL algorithms for healthcare decisioning.
• Define state representations and action spaces for policy learning.
• Maintain reward shaping and constraint mapping within the RL objective.
• Build training pipelines and monitor model performance in production.
• Collaborate with data engineers and governance teams for reproducibility.
• Instrument experiments with MLflow tracking and dashboards.

Skills

Reinforcement Learning

Production RL systems

PyTorch or TensorFlow

Ray RLlib

Distributed training

Data engineering collaboration

Python 3.x

Experiment tracking

Education

Bachelor's degree in computer science or related field

Tools

Databricks

PySpark

Delta Lake

MLflow

Redis

Kafka

Job description

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

You will own the full RL lifecycle—from feature engineering to distributed training, evaluation, and production deployment—ensuring decisions are explainable and auditable. The role combines healthcare outcomes with decision engineering, requiring collaboration with data, platform, and governance teams to