YO IT Consulting
Go QA Lead - Remote

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Go QA Lead - Remote
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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, DevOps tooling, code review, software QA, or technical mentoring.
- Strong understanding of Go fundamentals such as goroutines, 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, GitHub, 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, GitHub, 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.


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Key responsibilities
- 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.
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