Senior Decision Intelligence Engineer (NBA)

** Become a part of our caring community*
* Become a part of our caring community and help us put health first. We are looking for a skilled Decision Intelligence Engineer to design, train, and improve the reinforcement learning policy at the heart of Humana's Next Best Action platform.

This role is hands-on and research-oriented. You will design and evaluate decision-making algorithms, and instrument training pipelines. Additionally, you will collaborate with data and platform engineers. Furthermore, you will ensure the system operates correctly within the constraints of clinical eligibility rules and program-specific objectives.

The Senior Decision Intelligence Engineer Is involved in all stages of software development, including front-end development, back-end development, database integrations, network and hosting management, user interface, user experience, and back-end server management. Begins to influence department's strategy. Makes decisions on moderately complex to complex issues regarding technical approach for project components, andwork is performed without direction. Exercises considerable latitude in determining objectives and approaches to assignments.

** Use your skills to make an impact*
* ** Required Qualifications*
* + 5+ years (post undergraduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users.

+ 2+ years (post graduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users.

+ 2+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production. Relevant backgrounds include policy gradient and value-based RL (PPO, A3C, DQN, CQL), stochastic dynamic programming, discrete-event simulation, or large-scale combinatorial or constrained optimization.

+ Deep familiarity with Markov Decision Processes, Bellman-equation-based value estimation, reward or objective shaping, exploration-exploitation tradeoffs, and constraint formulation in real-world decision systems.

+ Demonstrated ability to diagnose failure modes in learned or optimized policies: instability, poor credit assignment across long horizons, and distributional shift across large populations.

+ Proficiency in Python 3.x; experience with PyTorch or Tensor Flow for policy network or learned model implementation.

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Experience with Ray RLlib or equivalent distributed computation frameworks for large-scale training or optimization.

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Experience with Databricks, PySpark, and Delta Lake for large-scale ML or data pipelines processing tens of millions of records.

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Experience with MLflow for experiment tracking, model registry, and artifact management.

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Experience with shipping systems that operate reliably under production load, not just research or prototype work.

** Preferred Qualifications*
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Experience with multi-agent RL frameworks (Petting Zoo or equivalent) or multi-agent simulation and coordination methods.

+ Familiarity with operations research methods applicable to constrained sequential decisioning: linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming.

+ Experience operating decision or optimization systems in regulated domains (healthcare, finance, or insurance) where member safety, auditability, and explainability are requirements.

+ Experience building simulation environments using Gymnasium, Sim Py, Any Logic, or equivalent frameworks for policy evaluation and backtesting.

+ Familiarity with event-driven feedback loops and how disposition signals feed retraining or re-optimization pipelines.

+ Open Telemetry instrumentation experience for ML or optimization pipeline observability.

** Work Style:
** Remote/Hybrid - Preferably Boston, MA.

Occasional travel to Humana's Tech Hubs for training or meetings may be required.

** Work Hours** :
Typical business hours are Monday-Friday, 8 hours/day, 5 days/week-- some flexibility might be possible, depending on business needs.

Very minimal travel might be required for training, meetings, and/or conferences

** Work at Home Requirements*
* WAH requirements:
Must have the ability to provide a high-speed DSL or cable modem for a home office. Associates or contractors who live and work from home in the state of California will be provided with payment for their internet expense.

A minimum standard speed for optimal performance of 25x10 (25mpbs download x 10mpbs upload) is required.

Satellite and Wireless Internet service is NOT allowed for this role.

A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information

** Interview Format*
* As part of our hiring…