Member of Technical Staff (MLOps)

Location: Palo Alto, United States; on-site

Company

hirify.global is an early-stage organization developing research and product environments supported by AI infrastructure and internal platforms.

What you will do

• Design, build, and maintain core infrastructure for research and product environments, including cloud compute, storage, CI/CD, observability, and security.
• Develop internal developer tools, automation systems, shared libraries, and platform services that improve engineering productivity.
• Build reliable systems for data management, model experimentation, evaluation, and deployment across research and production.
• Define and improve build, test, and release workflows from prototype through production.
• Monitor observability and performance metrics, diagnose bottlenecks, and optimize engineering workflows.
• Contribute to infrastructure strategy and architecture as the organization scales.

Requirements

• Strong software engineering background with experience in infrastructure, platform, or DevOps engineering.
• Experience with cloud environments such as AWS or GCP, Docker, Kubernetes, and CI/CD systems.
• Experience building developer tools, automation frameworks, or internal platforms.
• Familiarity with data pipelines, job scheduling, and ML experimentation workflows.
• Strong problem-solving and communication skills, with the ability to collaborate with research scientists and product engineers.
• Ability to work on-site in Palo Alto.

Nice to have

• Experience with machine learning infrastructure, training pipelines, or model evaluation tooling.
• Background in monitoring and observability tools such as Prometheus, Grafana, or Datadog.
• Knowledge of infrastructure as code and configuration management best practices.
• Interest in reproducible, safe, and efficient AI development.
• Experience at an early-stage startup or small research organization.

Culture & Benefits

• Close collaboration with research scientists and software engineers.
• Work focused on improving reproducibility, reliability, and efficiency across research and product workflows.
• Opportunity to shape infrastructure strategy and architecture as the organization grows.