Senior ML Engineer – AI Scheduling & Optimization

Flexzo Ai; Texas; On-site; USD 130,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Equity opportunities

Ownership over product architecture

Startup environment

Job summary

Flexzo AI is building AI-powered scheduling and optimization for healthcare staffing. The Senior Machine Learning Engineer will design, develop, and deploy ML models and AI-driven scheduling algorithms to improve shift coverage, clinician matching, and workforce utilization.

You will work on data pipelines, model monitoring, and integration with the scheduling platform. The role requires hands-on experience with production ML systems, large datasets, and optimization techniques, with preferred

Qualifications

• BS/MS in CS/ML/Data Science or related
• 5+ years in ML/AI engineering with production deployment experience
• Strong Python and ML framework skills
• Experience with scheduling/optimization is a plus

Responsibilities

• Design and deploy ML models for production scheduling systems
• Develop AI-driven scheduling algorithms to optimize workforce utilization
• Build data pipelines and model monitoring for production readiness
• Collaborate with product and engineering teams to integrate ML models
• Research emerging AI technologies to enhance scheduling capabilities

Skills

Python programming

Production ML deployment

Optimization algorithms

Data analysis

Education

Bachelor's or Master's in CS/ML/Data Science

Tools

PyTorch

TensorFlow

scikit-learn

SQL

AWS/GCP/Azure

Job description

Flexzo AI is building AI-powered scheduling and optimization for healthcare staffing. The Senior Machine Learning Engineer will design, develop, and deploy ML models and AI-driven scheduling algorithms to improve shift coverage, clinician matching, and workforce utilization.

You will work on data pipelines, model monitoring, and integration with the scheduling platform. The role requires hands-on experience with production ML systems, large datasets, and optimization techniques, with preferred