SRE for AI Research Infrastructure

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

Job summary

Northeastern University’s Khoury College and the National Deep Inference Fabric (NDIF) invite an experienced Site Reliability Engineer to strengthen production systems and scale a research platform. You will own reliability, build observability, and drive automation across distributed services, with emphasis on AI safety and interpretability.

The role emphasizes hands-on engineering, collaboration with the NDIF team, and advancing CI/CD, IaC, and debugging workflows in a fast-moving AI research

Qualifications

• Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.
• At least 3 years of experience in site reliability engineering or a closely related role.
• Strong Python skills and backend services experience.
• Experience with observability tooling such as Prometheus and Grafana, or equivalent tools.

Responsibilities

• Measure and improve the reliability, availability, and performance of the platform used by research teams.
• Build and maintain observability systems to detect issues early and enable proactive debugging.
• Improve CI/CD, automated testing, and infrastructure as code tooling.
• Create workflows for debugging across the distributed stack, including Ray connectivity, GPU scheduling, and resource allocation.
• Partner with the NDIF team to keep the system dependable and usable as needs grow.
• Establish and advocate for operational best practices as the platform scales.

Skills

Python

Distributed systems

CI/CD

Observability

Infrastructure as Code

Production systems

Communication

Education

Bachelor's degree

Equivalent practical experience

Tools

Prometheus

Grafana

Ray

Job description

Northeastern University’s Khoury College and the National Deep Inference Fabric (NDIF) invite an experienced Site Reliability Engineer to strengthen production systems and scale a research platform. You will own reliability, build observability, and drive automation across distributed services, with emphasis on AI safety and interpretability.

The role emphasizes hands-on engineering, collaboration with the NDIF team, and advancing CI/CD, IaC, and debugging workflows in a fast-moving AI research