(US) Principal ML System Engineer (AI)

Location: Remote, USA

Salary: $195,000–$217,000 base salary per year, plus bonus and benefits

Company

hirify.global develops healthcare technology products and centralized machine learning platform capabilities for traditional ML and hybrid ML/LLM solutions.

What you will do

• Define the technical vision, strategy, and multi-quarter roadmap for the company-wide machine learning platform.
• Establish reference architectures and standards for scalable data and ML pipelines covering training, evaluation, deployment, and serving.
• Set MLOps practices for model CI/CD, model registries, feature stores, experiment tracking, and build-versus-buy decisions.
• Design reliability, observability, performance, monitoring, alerting, and automated remediation practices for production ML systems.
• Define secure integration and infrastructure patterns connecting the platform to existing systems, APIs, and data sources.
• Provide technical leadership and mentorship across engineering teams and influence the organization-wide ML infrastructure roadmap.

Requirements

• Expert-level Python and Java skills with strong software engineering fundamentals.
• Extensive experience designing and building ML platforms and MLOps workflows at scale.
• Experience with MLFlow, Kubeflow, Ray, model-serving frameworks, or equivalent technologies.
• Extensive experience with cloud platforms such as AWS, Azure, and/or GCP, plus Docker and Kubernetes.
• Demonstrated experience setting technical direction and driving initiatives across multiple engineering teams.
• Experience with security architecture, including authentication, role-based access control, audit logging, and compliance monitoring.

Nice to have

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field.
• Familiarity with Azure Machine Learning, Databricks, serverless environments, and ML frameworks.
• Experience leading and sustaining critical cross-team systems.
• Experience with multi-factor authentication, network security, and compliance monitoring at scale.
• Experience optimizing large-model training and inference, including LLM serving, for performance and cost.

Culture & Benefits

• Full-time remote work from the USA.
• Bonus and benefits are included in the total rewards package.
• Compensation is assessed according to experience, skills, and market context.