Site Reliability Engineer

About the Opportunity

About Khoury College

Khoury College is committed to building a community of belonging among students, faculty and staff. We strongly encourage applicants from individuals with disabilities, veterans, and those with varied experiences and backgrounds. We invite you to join a fast-moving, ambitious college with an underlying mission that is driven by our motto of “CS for Everyone.”

About the National Deep Inference Fabric

The National Deep Inference Fabric (NDIF) is an artificial intelligence (AI) research infrastructure project funded by $9 million from the National Science Foundation (NSF) and more than $4 million from private foundations. Its mission is to create an innovative, highly transparent, large-scale AI inference infrastructure that enables cutting-edge scientific research on the largest open AI models.

Job Summary

NDIF is seeking an experienced Site Reliability Engineer to advance the reliability and coordinate the day-to-day operations of its growing research platform. This role creates systems that keep distributed services healthy and available, makes clusters easier to debug and operate, and builds tooling that enables the platform to scale as demand and deployments grow. This is a hands-on role with significant latitude for assessing complex technical problems, determining priorities, and delivering innovations with minimal oversight. A research background is not required, but knowledge of machine learning operations (MLOps) is highly desirable. The successful candidate will have experience operating production systems, a commitment to operating them well, and motivation to contribute to AI safety and interpretability.

Key Responsibilities & Accountabilities

• Measure and improve the reliability, availability, and performance of the platform used by research teams
• Build and maintain observability systems so issues are detected early and debugging is proactive rather than reactive
• Improve operational tooling and automation, including continuous integration and continuous delivery (CI/CD), automated testing, and infrastructure as code
• Create effective workflows for debugging and resolving issues across the distributed stack, including Ray connectivity, graphics processing unit (GPU) and resource allocation, and scheduling
• Partner with the broader NDIF team to keep the system dependable and usable as user needs evolve
• Establish and advocate for operational best practices as the platform grows

Minimum Qualifications

Required

• Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience

• At least 3 years of experience or more in site reliability engineering (SRE) or a closely related role

• Experience operating distributed systems in production, with a track record of owning complex systems end to end

• Strong Python skills and comfort working with backend services

• Experience with observability tooling such as Prometheus and Grafana, or equivalent tools

• Working command of CI/CD pipelines, automated testing, and infrastructure as code

• Judgment to debug and optimize systems with minimal guidance, along with communication skills that keep teams aligned

• Genuine interest in AI safety, interpretability, and supporting interdisciplinary research

Preferred

• Knowledge of MLOps

Required Documents

• Resume

Position Type

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.

Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information.

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

Compensation Grade/Pay Type:

Expected Hiring Range:

With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.