Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with accelerator ecosystems (GPU/CUDA, TPU, custom ASICs), including memory analysis, performance profiling, and runtime debugging using their toolchains (cuda-gdb, nsight-compute, nsight-systems) Familiarity with ML framework internals (PyTorch graph execution, torch.compile, operator dispatch) and AI compiler stacks (MLIR, LLVM, TVM, Triton) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with Linux kernel and driver-level debugging, hands-on experience with sanitizer and dynamic-analysis technology, or building comparable memory checking tools Experience working at the device-software boundary — accelerator runtime/driver internals, DMA and memory-mapped device behavior, on-device fault handling or firmware-assisted error reporting Experience with Linux debugging and profiling infrastructure (gdb, perf, eBPF, ftrace, coredump analysis, hardware performance counters) and familiarity with binary formats and debugging metadata (ELF/DWARF) Experience with compiler-based instrumentation and runtime shadow-memory techniques underlying sanitizers (LLVM instrumentation passes, interceptors, allocator red zones) Track record of building developer tools adopted by large engineering populations, ideally with contributions to open-source projects (LLVM sanitizers, gdb, Valgrind, Triton, etc.) Bachelor's degree + 5+ years, Master's degree + 3+ years, or PhD + 2+ years in systems software, developer tooling, or accelerator software development
$154,003/year to $217,000/year + bonus + equity + benefits
