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About the role
Become a part of our caring community and help us put health first.We are seeking a skilled Decision Intelligence Engineer to design, train, and continuously improve the reinforcement learning policy at the heart of Humana's Next Best Action platform.
What they're looking for
- Bachelor's degree in computer science or related field
- 8+ years of software engineering experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, or optimization engines serving millions of users
- 3+ years of hands-on experience implementing reinforcement learning or deep learning systems in production policy gradient methods (PPO, A3C), value-based approaches (DQN, Q-learning), or offline RL algorithms (CQL, Decision Transformer)
- Deep familiarity with the Bellman equation, reward shaping, exploration-exploitation tradeoff, and constraint mapping in real-world RL systems
- Demonstrated ability to diagnose RL-specific failure modes: policy collapse, credit assignment issues, and distributional shift across large populations
- Proficiency in Python 3.x, experience with PyTorch or TensorFlow for policy network implementation
More about this role
Become a part of our caring community and help us put health first.We are seeking a skilled Decision Intelligence Engineer to design, train, and continuously improve the reinforcement learning policy at the heart of Humana's Next Best Action platform. In this role you will own the full RL development lifecycle from feature engineering and reward design through distributed training, evaluation, and production deployment ensuring that every decision the platform makes for our 8 million members is informed by a policy that learns and improves with every interaction. You will work at the intersection of healthcare outcomes and decision engineering, translating member journey data into durable, explainable, and auditable decisioning intelligence.
This role is hands-on and research-oriented: you will implement and evaluate RL algorithms, instrument training pipelines, collaborate closely with data and platform engineers, and ensure the model operates correctly within the constraints of clinical eligibility rules and program-specific reward structures.
Design, implement, and evaluate RL algorithms suited to long-horizon, sparse-reward healthcare decisioning, including policy gradient...
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