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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Inference Performance Engineer - **Company:** d Matrix - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $175,000.0 - $265,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, C++ (Programming Language), Cloud Computing, Cloud Engineering, Nvidia CUDA, Software Debugging, Firmware, Python (Programming Language), Linux Kernel, PCI Express, Network Switches, Graphics Processing Unit (GPU), Large Language Models, Gpu Programming, Non-Uniform Memory Access, Discretization, Kubernetes, Bare Metal, SGLang, TensorRT, VLLM, Model Inference, Decoding - **Published:** October 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=21fd86b422fe4cbe ## About the Role * 8+ years of hands-on performance or systems engineering, including resolving hard regressions that crossed multiple layers of a stack. * Track record of finding performance overhead through profiling in unfamiliar, sparsely documented systems, valued more than experience with any specific vendor stack or accelerator. * Deep systems fundamentals, including Linux internals, NUMA, CPU pinning and cpusets, PCIe, memory and cache behavior, and network paths. * Experience running performance-sensitive workloads on Kubernetes, including identifying when scheduler defaults, network overlays, or orchestration abstractions cost latency or throughput, and closing the gap. * Working knowledge of LLM inference serving, including batching, KV-cache, prefill/decode, quantization, and speculative decoding. * Proficiency in Python and C/C++, sufficient to instrument, debug, and patch code across the stack. * Clear technical writing that hardware, firmware, or platform teams can act on without context from your part of the stack. * Sound judgment about ownership, with examples of correctly handing off problems that were not yours to fix. * Fluency with AI and agentic tools for investigation, code search, log triage, and drafting instrumentation, with the discipline to verify output against real data., * Experience with LLM serving frameworks such as vLLM, SGLang, or TensorRT-LLM. * Hands-on experience building or tuning speculative decoding, disaggregated serving, or heterogeneous serving architectures. * Experience tuning KV-cache management, request and batch scheduling, or weight prefetch and residency in an LLM serving system. * GPU programming and profiling experience (such as CUDA, Nsight, NCCL), valued as transferable background. * Experience with pre-production or bring-up hardware, or in organizations where root causes routinely cross team boundaries. * Rust experience. * Startup experience or comfort in environments where priorities shift quickly. ## Description The Inference Cloud Engineering team builds the deployment and orchestration technology behind d-Matrix's inference services: an internal cloud where teams test and tune models, and a customer-facing Cloud with self-service inference endpoints and compute. As the team's Inference Performance Engineer, you will own end-to-end performance of the workloads we deploy, from the moment a request arrives to the moment a result is returned. The standard is that a workload on our orchestrated platform should perform at least as well as it does on bare metal. Today's workloads include speculative decoding and attention-FFN disaggregation across d-Matrix Corsair accelerators and GPUs, with more heterogeneous architectures coming. Because there is no fixed system to master, the job is a method: profile first, form a hypothesis, test it, and then change the system. You will own the outcome, not every fix. Many root causes sit with other teams, so you will fix what you understand well and hand off the rest with evidence the owning team can act on. What You Will Do * Diagnose performance problems across the full serving path, from orchestration (scheduling, placement, networking, autoscaling) to inference internals (batching, KV-cache, prefill/decode, serving framework, driver and runtime, interconnect, host OS and NUMA, accelerator). * Isolate each problem to a specific layer and owner, then decide whether to fix it directly, route it to the owning team, or both, with a workaround now and a tracked handoff for the permanent fix. * Write minimal, reproducible problem statements for cross-stack findings so owning teams can act without repeating the investigation. * Protect throughput, latency, and stability targets for orchestrated workloads by finding and closing gaps against bare metal, such as placement overhead, non-performance power defaults, and extra network hops. * Assess what it takes to scale from single deployments to fleet operation, including routing, placement, and shared-resource contention. * Capture each workload's node requirements (CPU placement, memory and NUMA locality, network path) in the orchestration layer's configuration so performant deployment is the default. * Build lightweight telemetry and benchmarks that reach into prototype stacks to isolate or hand off problems. * Turn recurring investigations into playbooks and mentor engineers in the profiling-first method.