Go Quality Assurance Lead for Remote Contract Role
Overview
This remote, hourly contractor role centers on Go and AI training projects, ensuring quality across AI-generated Go code and trainer outputs. As the Go Quality Assurance Lead, you assess code against project rubrics, delivering precise feedback and steering toward idiomatic Go patterns and safe concurrency. You’ll keep core documentation up to date and help onboard contributors to stay aligned with client expectations. SME Careers, a fast-growing AI Data Services company and a subsidiary of SuperAnnotate, relies on your leadership to elevate Go training data quality.
What You'll Do9
- 1Spot-check AI-generated Go code for compilation, runtime behavior, and concurrency safety, then deliver targeted feedback via direct messages.
- 2Review backend snippets, tests, and API implementations produced by AI for correctness and clarity in the Go ecosystem.
- 3Update trainers and QAs on Discord about guideline changes and Go-specific quality expectations.
- 4Answer questions on Go syntax, concurrency, error handling, interfaces, testing, and rubric interpretation.
- 5DM inactive contributors to re-engage them, track follow-ups, and flag availability issues.
- 6Create and maintain Go style guides, onboarding materials, calibration tasks, and QA documentation.
- 7Lead onboarding calls with contributors to explain project expectations, workflows, and Go review standards.
- 8Flag insecure, non-compilable, race-prone, or production-ready issues in Go recommendations.
- 9Identify recurring quality gaps and help build scalable QA processes for Go AI training projects.
Requirements10
- 1Bachelor’s or Master’s in Computer Science, Software Engineering, Information Technology, or equivalent professional software engineering experience.
- 2Excellent English communication to follow guidelines and provide technical feedback.
- 33+ years of professional Go development, backend engineering, cloud services, distributed systems, DevOps tooling, code review, QA, or technical mentoring.
- 4Strong understanding of Go fundamentals: goroutines, channels, interfaces, structs, methods, slices, maps, pointers, error handling, context, packages/modules, testing, and idiomatic style.
- 5Ability to evaluate Go content against rubrics and identify issues such as non-compilable code, incorrect concurrency patterns, goroutine leaks, race conditions, or incomplete explanations.
- 6Familiarity with Go tooling and ecosystems such as go test, gofmt, go vet, race detector, Go modules, HTTP servers, REST APIs, gRPC, Docker, Kubernetes, SQL drivers, GitHub, CI/CD, and cloud-native workflows.
- 7Experience leading or supporting remote teams of trainers, annotators, engineers, or QAs.
- 8Comfort working in fast-moving remote environments using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- 9Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, calibration tasks, and QA documentation.
- 10Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.
Who Should Apply
Ideal candidates bring hands-on Go code review experience in AI training contexts and are comfortable guiding remote contributors. You demonstrate meticulous attention to rubric adherence and a track record of improving Go code quality through actionable feedback. This role is less suitable for those lacking remote collaboration experience or with limited Go fundamentals. If your background does not include go tooling like go test, vet, or race detector, your fit may be lower.
Salary Insight
$70/hr (hourly contract rate). This pay level aligns with mid-senior Go QA leadership work on remote AI training projects; context reflects contract expertise rather than a full-time salary.
Location
Required Skills
Application Tip
Prepare a portfolio of Go code reviews with concrete examples highlighting issues like race conditions, non-compilable code, and rubric-based feedback to demonstrate your capability to lead Go QA in AI training contexts.
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How your application is processed
1Application received
Your resume and details are logged the moment you apply.
2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
3Employer sees qualified profiles only
Only candidates who clear screening move forward.
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