Senior Software Engineer, Machine Learning (AI)

TL;DR

Senior Software Engineer, Machine Learning (AI): Deploying and maintaining clinical AI models and MLOps pipelines to optimize hospital revenue and care quality with an accent on model scalability and production reliability. Focus on building MLOps tools, integrating ML models into the core platform, and optimizing system performance.

Location: Must be based in the United States

Salary: $190,000–$230,000 base

Company

hirify.global builds clinical AI that helps hospitals translate care into payment and recover missed revenue.

What you will do

• Deploy and maintain machine learning models and pipelines in production environments.
• Integrate ML models into the broader hirify.global platform in collaboration with data and application engineers.
• Craft and maintain MLOps tools, including CI/CD, delivery, and monitoring systems.
• Optimize model performance and scalability to ensure high reliability and efficiency.
• Build internal tools to improve the development workflow for data scientists.
• Contribute to the overall design and architecture of ML systems.

Requirements

• 5+ years of experience in machine learning engineering or MLOps roles.
• Strong proficiency in Python and SQL, with experience in PyTorch, TensorFlow, pandas, or Polars.
• Experience deploying ML models using Docker, Kubernetes, or similar technologies.
• Solid understanding of CI/CD best practices for machine learning.
• Experience with AWS and cloud-based machine learning services.
• Must be located within the US.

Nice to have

• Experience with the end-to-end ML project lifecycle from collection to monitoring.
• Experience deploying LLM systems into production environments.
• Previous work in the healthcare sector.
• Contributions to open-source projects.

Culture & Benefits

• Comprehensive Medical, Dental & Vision plans (75% premiums covered).
• Unlimited PTO and 10 paid holidays.
• 401(k) with traditional and Roth options and a 4% match.
• Generous paid parental leave of up to 12 weeks.
• Remote-first work environment for US-based employees.
• Fast-moving environment with minimal bureaucracy.