Lead Decision Intelligence Engineer

🕒 August 20

🇺🇸 United States – Remote

💵 $129.3k - $177.8k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 36%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Humana

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1961

🏥 Healthcare

🛡️ Insurance

⚕️ Healthcare Insurance

Healthcare • Insurance • Healthcare Insurance

Humana is a healthcare company dedicated to making a positive impact on the health of individuals, communities, and the healthcare system as a whole. With a focus on putting health first, Humana serves a diverse range of populations, including seniors and the military, providing Medicare Advantage HMO, PPO, and PFFS plans. Humana is committed to fostering a culture of belonging and mutual respect, offering competitive and flexible benefits to ensure the financial security of its employees and their families. The company prides itself on creating an inclusive workplace where everyone has the opportunity to succeed.

📋 Description

• Design, implement, and evaluate reinforcement learning algorithms for long-horizon, sparse-reward healthcare decisioning • Define and maintain member state representations and action spaces • Apply Bellman equation, reward shaping, and constraint mapping to encode clinical eligibility, suppression rules, and program objectives • Manage exploration-exploitation tradeoffs in a production healthcare environment • Build simulation and backtesting environments using historical member journey data • Diagnose and remediate policy collapse, credit assignment errors, and distributional shift • Define reward thresholds and automated evaluation gates in the nightly Databricks training workflow • Instrument training runs with MLflow tracking hyperparameters, reward curves, action distributions, and feature importance • Own the nightly Databricks workflow, including feature engineering, state vector normalization, distributed Ray RLlib training, and batch scoring of 8 million eligible members • Collaborate with Data Engineering to ensure correctly joined inputs, accurate reward signals, and reproducible, auditable pipelines • Write production-quality PySpark feature engineering jobs and maintain data lineage through Databricks Unity Catalog • Manage model artifacts, versioning, and lifecycle in the MLflow Model Registry, including rollback capability • Apply multi-agent RL concepts where household or population-level coordination is required • Implement hard business rules as constraints within the RL objective • Collaborate with the Rules Engine team to align Drools eligibility guards and RL policy priorities • Integrate model outputs with the real-time decisioning hot path and Redis-cached recommendations • Define feedback loop contracts from Kafka through Databricks Delta Live Tables into subsequent training cycles • Document model behavior, limitations, and failure modes for clinical and compliance stakeholders • Support explainability requirements for member-facing decisions • Use AI-assisted engineering tools for scaffolding, testing, and documentation while keeping core model logic and reward design human-authored and peer-reviewed

🎯 Requirements

• 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 • Experience with 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 • Ability to diagnose policy collapse, credit assignment issues, and distributional shift across large populations • Proficiency in Python 3.x • Experience with PyTorch or TensorFlow • Experience with Ray RLlib • Experience with Databricks, PySpark, and Delta Lake for large-scale ML pipelines • Experience with MLflow for experiment tracking, model registry, and artifact management • Track record of shipping reliable ML systems under production load • This role is not eligible for work visa sponsorship • Ability to work typical business hours, Monday-Friday, 8 hours/day, 5 days/week • Minimum home internet speed of 25 Mbps download and 10 Mbps upload for home or hybrid home/office employees • Ability to work from a dedicated space without ongoing interruptions to protect member PHI/HIPAA information

🏖️ Benefits

• Bonus incentive plan based on company and/or individual 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 • Home or hybrid home/office work arrangement • Dedicated workspace support for protecting PHI/HIPAA information