Go QA Lead - Remote

Job Title: Go Quality Assurance Lead

Job Type: Contract

Location: Remote

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.

• 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 modules, HTTP servers, REST APIs, gRPC, Docker, Kubernetes, SQL drivers, Git Hub, CI/CD, and cloud-native workflows is preferred.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, coding mentors, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using Discord, Google Sheets, Google Docs, trackers, dashboards, Git Hub, and project management systems.
• Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation.
• Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.

• Quality monitoring:
  Spot-check Go items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Code review:
  Evaluate AI-generated Go code, debugging responses, backend snippets, concurrency examples, tests, API implementations, and technical explanations for correctness and clarity.
• Trainer and QA communication:
  Update trainers/QAs on Discord about guideline changes, workflow updates, and Go-specific quality expectations.
• Question handling:
  Respond to trainer/QA questions around Go syntax, concurrency, error handling, context usage, interfaces, testing, performance, security, and rubric interpretation.
• Trainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Documentation:
  Create and maintain Go style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding and training:
  Schedule and run onboarding/training calls with contributors to explain project expectations, workflows, rubrics, and Go review standards.
• Risk and security review:
  Flag insecure, misleading, non-compilable, race-prone, or non-production-ready Go recommendations.
• Process improvement:
  Identify recurring quality gaps and help build scalable QA processes for Go AI training projects.