Site Reliability Engineer - AI Research Infrastructure

Northeastern University; Boston (MA); On-site; USD 88,000 - 124,000; Full time

Job summary

Northeastern University through the NDIF project seeks a Site Reliability Engineer to improve reliability and coordinate day-to-day operations of its research platform. This hands-on role focuses on keeping distributed services healthy, debugging, and scaling deployments with latitude for solving complex problems.

A background in ML ops is desirable. The successful candidate will have strong Python skills, experience with observability tools like Prometheus and Grafana, and familiarity with

Qualifications

• Bachelor's degree in computer science, engineering, or a related field or equivalent practical experience.
• At least 3 years of site reliability engineering or related experience.
• Experience operating distributed systems in production and owning complex systems end to end.
• Strong Python skills and backend service experience.
• Experience with observability tooling such as Prometheus and Grafana.
• Working with CI/CD pipelines, automated testing, and infrastructure as code.
• Good debugging, judgment, and cross-team communication.
• Genuine interest in AI safety and interpretability.

Responsibilities

• Measure and improve reliability, availability, and performance of the platform.
• Build and maintain observability systems to enable proactive debugging.
• Improve CI/CD, automated testing, and infrastructure as code tooling.
• Develop workflows for debugging across the distributed stack (Ray, GPU, resource allocation, scheduling).
• Partner with NDIF team to keep systems dependable as user needs evolve.
• Advocate for operational best practices as the platform scales.

Education

Bachelor's degree in computer science, engineering, or a related field

Tools

Prometheus

Grafana

CI/CD tooling

Job description

Northeastern University through the NDIF project seeks a Site Reliability Engineer to improve reliability and coordinate day-to-day operations of its research platform. This hands-on role focuses on keeping distributed services healthy, debugging, and scaling deployments with latitude for solving complex problems.

A background in ML ops is desirable. The successful candidate will have strong Python skills, experience with observability tools like Prometheus and Grafana, and familiarity with