Degree must be completed prior to joining Meta Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience 2+ years of experience in software engineering, with exposure to systems software, developer tooling, or infrastructure Proficiency in C++ and Python, including systems-level programming concepts Experience working across multiple layers of a system stack (e.g., application, runtime, OS/driver, or hardware interfaces) Track record of independently delivering software projects from design through production deployment Experience debugging and resolving issues in complex software systems (e.g., using log analysis, stack traces, or system-level diagnostic tools) Experience building developer tools such as debuggers, profilers, build systems, CLI tools, monitoring dashboards, or diagnostic utilities Exposure to accelerator ecosystems (GPU/CUDA, TPU, custom ASICs) or heterogeneous computing environments Familiarity with Linux debugging and profiling tools (gdb, perf, strace, eBPF, valgrind) or similar diagnostic infrastructure Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Contributions to open-source projects demonstrating tooling or system software interest Experience in using data-driven methods to evaluate tooling effectiveness and to prioritize improvements Experience with distributed systems debugging, profiling, or monitoring at scale Familiarity with ML frameworks (PyTorch, TensorFlow) or compiler infrastructure (LLVM, MLIR, TVM)Meta builds technologies that help people connect, find communities, and grow businesses. The Tooling team provides debugging, profiling, memory analysis, and monitoring capabilities for the whole MTIA Ecosystem, redefining ML accelerator tooling by leveraging Meta's full-stack ownership from silicon specs to fleet observability.