perplexity - Member of Technical Staff (AI Inference Engineer)

• Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. • You understand modern LLM architectures and are able to bring them up reliably in a production environment. • You've built and operated production distributed systems under real load - ideally performance-critical ones. • Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. • You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. • Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. • ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. • Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. • Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. • Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. • Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. • 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. • Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). • Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). • Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation). • Final offer amounts are determined by multiple factors including experience and expertise. • Equity: In addition to the base salary, equity may be part of the total compensation package.