C++ Team Lead

In this hourly, remote contractor role, you will work as a C++ Quality Assurance Lead (QAL) 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. This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and, * Quality monitoring: Spot-check C++ items, identify issues, provide feedback through DMs, and escalate 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.

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. 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. requirements

• 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.