Member of Technical Staff (AI Inference Engineer)
Required skills
Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). 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.
Preferred skills
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.
Technologies
Rust, Python, CUDA, CuTe DSL.
Responsibilities
New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
Seniority
3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
Domain
AI Inference, Machine Learning, Distributed Systems, GPU Programming, Large Language Models.