Lead RL Engineer - Healthcare Decision Intelligence

Humana; Boston (MA); Hybrid; USD 129,000 - 178,000; Full time

Job summary

Humana is seeking a Decision Intelligence Engineer to design, train, and improve reinforcement learning policies for the Next Best Action platform. You will own the full RL lifecycle, from feature engineering to production deployment, ensuring decisions for millions of members are informed by robust, auditable policies.

The role emphasizes hands-on RL algorithm implementation, model evaluation, and collaboration with data/ platform engineers to support clinical eligibility rules and reward

Qualifications

• Bachelor's degree in computer science or related field.
• 8+ years of software engineering experience building large-scale production systems.
• 3+ years implementing reinforcement learning or deep learning in production.
• Proficiency in Python 3.x and ML frameworks (PyTorch or TensorFlow).
• Experience with Ray RLlib, Databricks, PySpark, Delta Lake, and MLflow.
• Track record shipping ML systems under production load with auditability.

Responsibilities

• Design, implement, and evaluate RL algorithms for healthcare decisioning.
• Own the end-to-end RL development lifecycle from feature engineering to deployment.
• Instrument training runs with tracking of hyperparameters and metrics in MLflow.
• Collaborate with data/platform engineers to ensure reproducible pipelines.
• Maintain safety, explainability, and compliance within production systems.

Skills

Reinforcement learning

Python 3.x

PyTorch or TensorFlow

Ray RLlib

Databricks / PySpark

Delta Lake

MLflow

Production ML systems

Education

Bachelor's degree in computer science or related field

Tools

Ray RLlib

Databricks

PySpark

Delta Lake

MLflow

Job description

Humana is seeking a Decision Intelligence Engineer to design, train, and improve reinforcement learning policies for the Next Best Action platform. You will own the full RL lifecycle, from feature engineering to production deployment, ensuring decisions for millions of members are informed by robust, auditable policies.

The role emphasizes hands-on RL algorithm implementation, model evaluation, and collaboration with data/ platform engineers to support clinical eligibility rules and reward