Senior C++ Code Reviewer

About OpenTrain

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About AI Training Work

AI training (also called data labeling or annotation) is the human side of building modern AI systems. Contributors shape how models behave by reviewing and refining examples—here, you'll help ensure AI-written C++ is correct, secure, and high quality so it can be used to train next-generation models.

The Role

You will audit annotator reviews of AI-generated C++ code: compile and run submitted snippets in sandboxed containers, verify they follow the prompt and function correctly across build configurations, and ensure they meet performance and security best practices. When reviews are incorrect or incomplete you will correct ratings, provide targeted written feedback, and enforce the project's quality rubric to safeguard training data.

This is a remote, contractor, part-time role requiring 20+ hours per week. Compensation is PAY_PER_HOUR at USD 25/hr. Work may be performed worldwide.

• Data type: COMPUTER_CODE_PROGRAMMING; label types: COMPUTER_PROGRAMMING_CODING.
• (you will work in sandboxed containers and project-specific tools).
• Employment: Contractor, Part-time; time requirement: 20+ hours/week.

What You'll Do

Carry out hands-on technical review and quality assurance of AI-generated C++ submissions and the human annotator reviews attached to them.

• Compile and run code in sandboxed containerized environments across multiple build configurations (GCC/Clang/MSVC where applicable).
• Use sanitizers (ASan/UBSan/TSan), Valgrind, and debuggers to detect undefined behavior, memory leaks, and concurrency issues.
• Validate that code meets the prompt requirements and correct functional or logic errors in annotator ratings.
• Assess performance and recommend optimizations using profiling tools (perf, VTune) and knowledge of cache-friendly design and SIMD/vectorization.
• Ensure secure-coding practices: check for buffer overflows, integer overflow, and other CWE-class vulnerabilities and confirm exploit mitigations are considered.
• Provide concise, constructive feedback to annotators and ensure every review adheres to the project's rubric and checklist.

Requirements

You must be able to perform advanced technical review work independently and communicate clear, actionable feedback in written English (B2+ CEFR). Below are the specific required skills and experience drawn from the project brief.

• 7+ years in professional C++ development, QA, or code-review roles.
• Expert knowledge of modern C++ (C++17/20/23): constexpr, ranges, concepts, STL, templates, move semantics, RAII, and smart-pointer paradigms.
• Proven ability to detect memory leaks, undefined behavior, race conditions; strong grasp of threads, atomics, std::async/futures, and lock-free patterns.
• Advanced testing and debugging experience with GoogleTest/Catch2, ASan/UBSan/TSan, Valgrind, gdb/lldb, and coverage tools.
• Performance optimization skills: profiling with perf/VTune, cache-aware design, SIMD/vectorization, and cross-compiler tuning.
• Secure-coding familiarity: CWE/OWASP issues for C/C++, integer overflows, buffer overruns, and exploit mitigations (stack canaries, ASLR).
• Proficiency with build systems and toolchains: CMake/Bazel, cross-platform builds, Dockerized toolchains, and CI/CD (GitHub Actions, GitLab CI).
• Comfort compiling and executing code in sandbox/container environments to verify functionality and prompt compliance.
• Experience with rubric-based scoring, checklist reviews, and ticketing/annotation tools (Jira, Asana).
• Excellent written English (B2+ CEFR) for delivering clear feedback and mentoring peers.

Nice-to-Have Experience

The following are preferable but not required for consideration; they make you an especially strong candidate for projects that overlap with AI evaluation pipelines.

• Background in LLM evaluation, RLHF pipelines, or prior work on AI/ML data-labeling projects.

Who Should Apply and How It Works

Apply if you are a seasoned C++ engineer or reviewer who enjoys hands-on debugging, security analysis, and mentoring annotators to improve dataset quality. This role is a great fit for contractors looking for flexible, remote, part-time work that directly impacts how AI models are trained.

To apply, create a free OpenTrain account, build your profile, and submit your application through the project listing. OpenTrain connects you to the project; selected candidates will be invited to the next steps for onboarding and sample verification.