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.

Good if you touched any of

• 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).