About This Role
In this hourly, remote contractor role, you will work as a C++ Quality Assurance Lead to oversee quality, consistency, and trainer performance across C++ AI training projects. You will review AI-generated C++ 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, memory safety, performance, algorithmic reasoning, debugging accuracy, readability, maintainability, formatting, instruction-following, and adherence to project-specific rubrics. This role requires strong C++ expertise, English communication skills, excellent attention to detail, and the ability to manage quality workflows across remote technical teams. The company 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 C++ quality leadership will help ensure C++ training data is accurate, compilable, efficient, safe, clearly explained, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. 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, providing you with access to future projects available through our expert network.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or equivalent professional software engineering experience.
- Strong grasp of English to follow guidelines and provide clear technical feedback.
- 3+ years of professional experience in C++ development, systems programming, performance engineering, embedded software, backend engineering, code review, QA, or technical mentoring.
- Strong understanding of modern C++ standards, RAII, smart pointers, templates, STL containers/algorithms, object lifetime, move semantics, concurrency, exceptions, memory management, and build systems.
- Ability to identify issues such as undefined behavior, memory leaks, dangling references, race conditions, inefficient algorithms, non-compilable code, hallucinated APIs, or incomplete explanations.
- Familiarity with CMake, GCC/Clang/MSVC, GDB/LLDB, sanitizers, Valgrind, GoogleTest, Catch2, Boost, GitHub, CI/CD, profiling, and static analysis tools is preferred.
- Experience leading or supporting remote teams of trainers, reviewers, engineers, coding mentors, or QAs is strongly preferred.
- Comfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- Highly organized and able to maintain style guides, FAQs, trackers, 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.
Key Responsibilities
- Quality monitoring: Spot‑check C++ items, identify issues, provide feedback through DMs, and elevate recurring or critical quality problems.
- Code review: Evaluate AI-generated C++ code, debugging responses, algorithmic solutions, tests, explanations, and performance recommendations.
- Trainer/QA communication: Update contributors on Discord about guideline changes, workflow updates, and C++-specific review standards.
- Question handling: Respond to questions around memory safety, compilation, templates, STL usage, concurrency, complexity, testing, and rubric interpretation.
- Activation management: DM inactive contributors, track follow‑ups, and flag availability issues.
- Documentation: Create and maintain C++ style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding: Run onboarding/training calls covering project expectations, rubrics, and C++ quality standards.
- Risk review: Flag unsafe, non‑compilable, misleading, inefficient, or non‑production-ready C++ recommendations.
- Process improvement: Identify recurring quality gaps and build scalable QA processes.