Senior Decision Intelligence Engineer

Become a part of our caring community   The Senior Machine Learning Engineer, Decision Intelligence is a hands-on individual contributor responsible for building, deploying, and operating ML and decisioning pipelines for the NBA Decision Intelligence Platform. This role focuses on production pipeline development, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning. You will help ensure the platform selects the right action for the right member while respecting clinical eligibility, suppression rules, channel constraints, program goals, and operational capacity. You will work closely with ML engineers, data engineers, platform engineers, product owners, and decision engine teams to deliver reliable, scalable, and auditable production systems. Additional Job Description Required Qualifications Bachelors in computer science or relevant field 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.

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. Use your skills to make an impact   Additional Information Work Style: Remote/Hybrid - Preferably Boston, MA. Occasional travel to Humana's offices 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