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We are seeking a Lead Machine Learning Engineer to architect, develop, and manage machine learning systems that enable real-time, personalized decision-making across digital and assisted channels. These platforms leverage predictive modeling, optimization algorithms, and contextual data to identify the most relevant next action for each individual—at scale, with minimal latency, and with a strong emphasis on reliability and explainability.
This position centers on production machine learning engineering. You will lead the creation of ML pipelines, online inference services, and decision-time scoring logic, utilizing AI-assisted and agentic solutions to enhance development velocity, model quality, and operational effectiveness.
Key Responsibilities
ML System Design & Architecture
- 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.
Decision-Focused Modeling
- 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.
AI-Enabled & Agentic Efficiency
- 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.
MLOps & Model Operations
- 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.
Collaboration & Leadership
- 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.
Role Significance
As reliance on automated and intelligent decisions increases, the integrity of machine learning engineering becomes critical to organizational trust, performance, and user experience. This role ensures that machine learning systems are not only accurate but also reliable, interpretable, and efficient to develop and maintain.
Use your skills to make an impact
Required Qualifications
- 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
- Model training and evaluation
- 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
Preferred Qualifications
- 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
Success Criteria
- Models deliver reliable performance in real-time decisioning workflows
- ML systems scale effectively without excessive operational burden
- Experimentation cycles are efficient, repeatable, and measurable
- Engineering teams have confidence in model outputs and their transparency
- AI-assisted tools drive measurable improvements in development speed and model quality
Additional Information
SSN Alert Statement
Humana values personal identity protection. Please be aware that applicants may be asked to provide their Social Security Number, if it is not already on file. When required, an email will be sent from Humana@myworkday.com with instructions on how to add the information into your official application on Humana’s secure website.
WAH Internet Statement
To ensure Home or Hybrid Home/Office employees’ ability to work effectively, the self-provided internet service of Home or Hybrid Home/Office employees must meet the following criteria:
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.
Satellite, cellular and microwave connection can be used only if approved by leadership.
Employees who live and work from Home in the state of California, Illinois, Montana, or South Dakota will be provided a bi-weekly payment for their internet expense.
Humana will provide Home or Hybrid Home/Office employees with telephone equipment appropriate to meet the business requirements for their position/job.
Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information.
Travel: While this is a remote position, occasional travel to Humana's offices for training or meetings may be required.
Scheduled Weekly Hours
40
Pay Range
The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.
$129,300 - $177,800 per year
This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.
Description of Benefits
Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole-person well-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave), short-term and long-term disability, life insurance and many other opportunities.
Application Deadline: 03-19-2026
About us
Humana Inc. (NYSE: HUM) is committed to putting health first – for our teammates, our customers and our company. Through our Humana insurance services and CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare, Medicaid, families, individuals, military service personnel, and communities at large.
Equal Opportunity Employer
It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.