Go Quality Assurance Lead — Remote Impact & Mentorship

About This Role

In this hourly, remote contractor role, you will work as a Go Quality Assurance Lead to oversee quality, consistency, and trainer performance across Go AI training projects. You will review AI-generated Go code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards. You will assess work for code correctness, compile-time validity, runtime behavior, concurrency safety, error handling, readability, maintainability, performance, security awareness, test coverage, formatting, instruction-following, and adherence to project-specific rubrics.

You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Go quality leadership will help ensure Go training data is accurate, executable, idiomatic, efficient, clearly explained, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Job Description

Job Title: Go Quality Assurance Lead

Job Type: Contract

Location: Remote

About This Role

In this hourly, remote contractor role, you will work as a Go Quality Assurance Lead to oversee quality, consistency, and trainer performance across Go AI training projects. You will review AI-generated Go code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards. You will assess work for code correctness, compile-time validity, runtime behavior, concurrency safety, error handling, readability, maintainability, performance, security awareness, test coverage, formatting, instruction-following, and adherence to project-specific rubrics.

You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Go quality leadership will help ensure Go training data is accurate, executable, idiomatic, efficient, clearly explained, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Your profile

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or equivalent professional software engineering experience.
• Strong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of professional experience in Go development, backend engineering, cloud services, distributed systems, Dev Ops tooling, code review, software QA, or technical mentoring.
• Strong understanding of Go fundamentals such as go routines, channels, interfaces, structs, methods, slices, maps, pointers, error handling, context, packages/modules, testing, and idiomatic Go style.
• Ability to evaluate Go content against detailed rubrics and identify issues such as non-compilable code, incorrect concurrency patterns, goroutine leaks, race conditions, poor error handling, inefficient logic, hallucinated APIs, or incomplete explanations.
• Familiarity with common Go tools and ecosystems such as go test, gofmt, go vet, race detector, Go…