Senior Staff Inference Performance Engineer

At d-Matrix , we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive , and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together , we can help shape the endless possibilities of AI.  Senior Staff Inference Performance Engineer Location: Santa Clara, CA / Remote, US Role Overview 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.

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.

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,