Networked Systems Researcher Research Coding Tasks

You will create and review technical coding tasks used to test AI models. Tasks should reflect current research questions and your experience in networked systems.

You will also review model outputs, give precise technical feedback, and keep tasks and evaluations at a research-quality standard. The work is independent and follows reproducible research practices.

• Build self-contained research coding tasks in your area of networked systems expertise.
• Review AI model outputs and provide technically precise feedback.
• Create implementation-focused tasks connected to current research questions.
• Work in areas such as congestion control, datacenter networking, programmable networks, or cluster scheduling.

What it pays and takes

This is remote contract work with an asynchronous schedule. Individual tasks are expected to take about four hours, so you can fit the work around your existing research.

• $50 per hour.
• Remote work open to candidates in the countries listed for this role.
• Professional fluency in English.
• Published research in networked systems at venues such as NSDI, SIGCOMM, IMC, CoNEXT, or comparable venues.
• Released research code through GitHub or another research artifact.
• A completed or in-progress PhD in computer science or a related field.
• Strong technical depth in at least one relevant area, including congestion control, datacenter networking, programmable networks, or cluster scheduling.
• Helpful background includes first-author publications, Docker experience, reproducible research environments, and research engineering that combines system design, implementation, and technical evaluation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human work behind systems that learn from examples, including coding tasks and reviews of AI-generated answers. People with research and technical expertise help make those examples accurate and useful.