Senior Reinforcement Learning Engineer (Remote)

Humana; United States; Hybrid; USD 107,000 - 147,000; Full time

Benefits offered by this job

Remote/Hybrid work (Boston preferred)

Travel to Humana tech hubs

Job summary

Humana seeks a Senior Decision Intelligence Engineer to design, train, and improve the reinforcement learning policy at the heart of the Next Best Action platform. The role combines hands-on research with production deployment, requiring collaboration with data and platform teams to meet clinical eligibility rules and program goals.

The candidate will tackle algorithm design, training pipelines, and evaluation while operating across front-end/backend concerns and ensuring robust, scalable

Qualifications

• 5+ years of software engineering or quantitative research in data-intensive platforms serving millions of users.
• 2+ years building and operating large-scale production systems in data-intensive environments.
• 2+ years implementing RL or simulation-driven decision systems in production.
• Familiarity with MDPs, Bellman-equation-based value estimation and exploration-exploitation tradeoffs.
• Ability to diagnose failure modes in learned policies and handle distributional shift.
• Proficiency in Python 3.x; experience with PyTorch or TensorFlow; familiarity with Databricks/PySpark.

Responsibilities

• Design, train, and improve reinforcement learning policy for Humana's Next Best Action platform.
• Design and evaluate decision-making algorithms and instrument training pipelines.
• Collaborate with data and platform engineers to ship production-ready solutions.
• Ensure systems operate within clinical eligibility rules and program objectives.

Skills

Reinforcement learning

Python

Ray RLlib

PyTorch / TensorFlow

Databricks / PySpark

Tools

Ray RLlib

Databricks

MLflow

TensorFlow

PyTorch

Delta Lake

Job description

Humana seeks a Senior Decision Intelligence Engineer to design, train, and improve the reinforcement learning policy at the heart of the Next Best Action platform. The role combines hands-on research with production deployment, requiring collaboration with data and platform teams to meet clinical eligibility rules and program goals.

The candidate will tackle algorithm design, training pipelines, and evaluation while operating across front-end/backend concerns and ensuring robust, scalable