Humana Inc. is committed to putting health first, and they are seeking a Lead Machine Learning Engineer to architect and manage machine learning systems for real-time personalized decision-making. The role involves leading the creation of ML pipelines and decision-time scoring logic to enhance operational effectiveness and model quality.
Responsibilities:
- Design and manage end-to-end machine learning systems, including:
- Feature engineering and reuse strategies
- Offline training pipelines
- Online inference and scoring services
- Model versioning, rollout, and rollback procedures
- Ensure systems meet stringent requirements for latency, scalability, reliability, and correctness in real-time contexts
- Define clear separation between model development, deployment, and downstream decision logic
- Build and operationalize models such as:
- Propensity or likelihood prediction
- Uplift or incremental impact models
- Engagement or responsiveness scoring
- Design models to be composable, explainable, and robust for automated decision workflows
- Collaborate with analytics and product teams to translate business objectives into measurable modeling outcomes
- Apply AI-assisted and agentic approaches to boost ML engineering productivity, including:
- Automated code generation and refactoring for pipelines and services
- Feature exploration and validation
- Intelligent experiment tracking and comparison
- Enhanced debugging and root-cause analysis
- Assess and adopt modern tools to accelerate experimentation, reduce manual overhead, and ensure reliable model operations
- Focus on implementing practical, production-ready AI tools
- Develop and sustain robust MLOps practices, including:
- Continuous training and deployment pipelines
- Online model monitoring for latency, drift, and stability
- Safe rollout strategies (e.g., canary, shadow, phased releases)
- Fallback mechanisms for model degradation or unavailability
- Guarantee model outputs are traceable, reproducible, and auditable
- Serve as the technical leader for ML engineering, establishing standards and best practices
- Partner with software engineers, data engineers, and platform teams to ensure seamless integration of ML systems into production
- Mentor machine learning engineers and contribute to the overall maturity of engineering teams
- Influence architectural decisions to ensure testability, observability, and resilience
Requirements:
- 8+ years of experience in machine learning engineering, applied ML, or data-driven platform development
- 3+ years in a technical lead or senior ML engineering capacity
- Deep expertise in feature engineering and data pipelines
- Deep expertise in model training and evaluation
- Deep expertise in real-time or near-real-time inference systems
- Strong software engineering skills in Python, Java, or similar languages
- Practical experience with AI-assisted development tools to streamline ML workflows
- Experience with personalization, recommendation, or decisioning platforms
- Familiarity with distributed systems and event-driven architectures
- Experience deploying models in regulated or high-reliability settings
- Knowledge of model explainability and fairness methodologies