Note: The job is a remote job and is open to candidates in USA. Humana is a healthcare company focused on helping people achieve their best health through insurance and healthcare services. The Senior Decision Intelligence Engineer will design, train, evaluate, and improve reinforcement learning policies powering the Next Best Action platform, while instrumenting training pipelines and collaborating with data and platform engineers. The role also ensures decision systems operate within clinical eligibility rules and program-specific objectives.
Responsibilities
- Design, train, and improve the reinforcement learning policy at the heart of Humana's Next Best Action platform
- Design and evaluate decision-making algorithms, and instrument training pipelines
- Collaborate with data and platform engineers
- 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
Skills
- 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 TensorFlow for policy network or learned model implementation
- Experience with Ray RLlib or equivalent distributed computation frameworks for large-scale training or optimization
- Experience with Databricks, PySpark, and Delta Lake for large-scale ML or data pipelines processing tens of millions of records
- Experience with MLflow for experiment tracking, model registry, and artifact management
- Experience with shipping systems that operate reliably under production load, not just research or prototype work
- Must have the ability to provide a high-speed DSL or cable modem for a home office
- 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
- At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested
- Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information
- Experience with multi-agent RL frameworks (PettingZoo 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, SimPy, AnyLogic, or equivalent frameworks for policy evaluation and backtesting
- Familiarity with event-driven feedback loops and how disposition signals feed retraining or re-optimization pipelines
- OpenTelemetry instrumentation experience for ML or optimization pipeline observability
- Work Style:** Remote/Hybrid - Preferably Boston, MA
Benefits
- Medical, dental and vision benefits
- 401(k) retirement savings plan
- Paid time off
- Company and personal holidays
- Paid parental and caregiver leave
- Short-term and long-term disability
- Life insurance
- Bonus incentive plan
Company Overview
Company H1B Sponsorship