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 programming 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, pointer logic, portability, performance, debugging accuracy, readability, formatting, instruction-following, and rubric adherence. This role requires strong C expertise, English communication skills, 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 foundation-model labs. Your C quality leadership will help ensure C training data is accurate, compilable, safe, 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.

Responsibilities

• Quality monitoring: Spot-check C items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Code review: Evaluate AI-generated C code, debugging responses, pointer/memory examples, algorithmic solutions, tests, and technical explanations.
• Trainer/QA communication: Update contributors on Discord about guideline changes, workflow updates, and C-specific review standards.
• Question handling: Respond to questions around pointers, memory allocation, compilation, UB, portability, embedded constraints, 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 for C contributors.
• Risk review: Flag unsafe, non-compilable, misleading, memory-unsafe, or non-production-ready C recommendations.
• Process improvement: Identify recurring quality gaps and improve QA workflows.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or equivalent professional experience.
• Strong grasp of English to follow guidelines and provide clear technical feedback.
• 3+ years of professional experience in C programming, systems programming, embedded development, firmware, operating systems, performance engineering, code review, QA, or technical mentoring.
• Strong understanding of pointers, arrays, memory allocation, structs, function pointers, preprocessor macros, header files, compilation/linking, undefined behavior, file I/O, bitwise operations, and portability.
• Ability to identify issues such as buffer overflows, memory leaks, use-after-free, undefined behavior, invalid pointer arithmetic, race conditions, non-compilable code, hallucinated APIs, or incomplete explanations.
• Familiarity with GCC/Clang/MSVC, Make/CMake, GDB/LLDB, Valgrind, sanitizers, embedded toolchains, POSIX APIs, unit testing frameworks, 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 PM systems.
• Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation.
• Experience with AI training, LLM evaluation, code QA, or rubric-based code review is a strong plus.