Machine Learning Operations Engineer (AI)

TL;DR

Machine Learning Operations Engineer (AI): Architecting and building robust infrastructure for applied AI products with an accent on continuous learning loops and production-grade model deployment. Focus on designing scalable training pipelines, optimizing inference systems, and creating the data flywheel that bridges the gap between user feedback and model improvement.

Location: Must be based in Mountain View, United States. Employees are expected to work from a designated Microsoft office at least four days a week.

Salary: $119,800 – $304,200 per year (range varies by location and level).

Company

hirify.global is focused on building world-class, applied AI products designed to improve continuously through robust infrastructure and intelligent systems.

What you will do

• Design and implement scalable training infrastructure for data ingestion and model versioning.
• Build the data flywheel to capture user interactions and route them back into training loops.
• Optimize model serving architecture, including latency, cost management, and intelligent caching.
• Develop deployment pipelines with automated testing, gradual rollouts, and rollback mechanisms.
• Partner with ML engineers and data scientists to build APIs that enable rapid feedback loops.

Requirements

• Must be based in or able to work from the Mountain View office.
• 6+ years of experience building and operating ML systems in production.
• 5+ years of software engineering fundamentals, including distributed systems, containerization, and cloud platforms.
• 5+ years of hands-on experience with ML orchestration tools like Airflow or Kubeflow.
• 5+ years of experience optimizing model inference and managing GPU utilization.
• Bachelor’s degree or higher in Computer Science, Statistics, or a related field.

Nice to have

• Familiarity with LLM deployment patterns, vector databases, and prompt management.
• Experience with RAG, fine-tuning pipelines, or evaluation frameworks.
• Ability to design holistic systems where data flows naturally through improvement cycles.

Culture & Benefits

• Commitment to a culture of inclusion, respect, integrity, and accountability.
• Growth mindset environment focused on innovation and collaboration.
• Access to comprehensive corporate benefits and compensation packages.