Senior Decision Intelligence Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, implement, and maintain machine learning and decision-making pipelines for the NBA Decision Intelligence Platform.

• Create production pipelines, MLOps workflows, feature engineering, scoring workflows, monitoring, and decision-making that accounts for optimization.

• Ensure the platform identifies the appropriate action for each member while considering clinical eligibility, suppression rules, channel limitations, program objectives, and operational capacity.

• Collaborate with machine learning engineers, data engineers, platform engineers, product owners, and decision engine teams.

• Provide dependable, scalable, and auditable production systems.

• Support production systems catering to millions of users.

• Engage in on-demand candidate assessments and the interview process.

⛳️ Requirements

• A bachelor's degree in computer science or a related field.

• Over 5 years of post-undergraduate experience in software engineering or quantitative research focused on building and operating large-scale production systems.

• At least 2 years of post-graduate software engineering or quantitative research experience in constructing and managing large-scale production systems.

• Minimum of 2 years of practical experience with reinforcement learning, operations research methods, or simulation-driven decision systems in a production environment.

• Knowledge of recommendation systems, optimization engines, simulation frameworks, or data-intensive platforms that serve millions of users.

• Strong understanding of Markov Decision Processes, Bellman-equation-based value estimation, reward or objective shaping, exploration-exploitation trade-offs, and constraint formulation.

• Capability to identify instability, inadequate credit assignment over long horizons, and distributional shifts across large populations.

• Proficiency in Python 3.x.

• Experience with PyTorch or TensorFlow.

• Familiarity with Ray RLlib or other distributed computation frameworks.

• Experience with Databricks, PySpark, and Delta Lake.

• Knowledge of MLflow.

• Experience in deploying systems that function reliably under production loads.

• Understanding of policy gradient and value-based reinforcement learning, including PPO, A3C, DQN, and CQL.

• Preferred: experience with multi-agent reinforcement learning frameworks such as PettingZoo.

• Preferred: familiarity with linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming.

• Preferred: experience managing decision or optimization systems in regulated domains.

• Preferred: experience in constructing simulation environments using Gymnasium, SimPy, AnyLogic, or equivalent tools.

• Preferred: familiarity with event-driven feedback loops.

• Preferred: experience with OpenTelemetry instrumentation.

• Availability to work typical Monday to Friday business hours.

• Minimum home internet speed of 25 Mbps download and 10 Mbps upload.

• A dedicated workspace free from interruptions to safeguard member PHI/HIPAA information.

🏝️ Benefits

• Bonus incentive plan based on individual and/or company performance.

• Medical benefits.

• Dental benefits.

• Vision benefits.

• 401(k) retirement savings plan.

• Paid time off.

• Company holidays.

• Personal holidays.

• Paid parental leave.

• Paid caregiver leave.

• Short-term disability.

• Long-term disability.

• Life insurance.

• Personal wellness support.

• Smart healthcare decision support.

• Remote/home or hybrid work arrangement.

• Occasional travel for training or meetings.

• Flexible work hours may be available based on business needs.