Staff GPU Inference SDET

Cerebras Systems•Sunnyvale, CA

About The Position

As a Staff GPU Inference SDET, you will be the founding quality, reliability, and validation lead for a new GPU Inference Development team. Working closely with engineering leads and cross-functional systems infrastructure teams, you will design, build, and scale the end-to-end release qualification and automated test ecosystem for our GPU inference stack and rack-scale accelerated compute fleets. In this high-impact role, you will be responsible for building automated test suites to validate multi-node GPU cluster bring-up, verifying prefill worker optimizations, testing open-source and custom serving engines, and ensuring numerical correctness and performance stability under real-world streaming workloads. You will be the primary technical anchor ensuring production-grade reliability, fault isolation, and peak inference performance across accelerated GPU infrastructure.

Requirements

8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer.
Hands-on experience bringing up, provisioning, and validating multi-node GPU clusters (NVIDIA or AMD ecosystem) across public cloud infrastructure or enterprise data center environments.
Deep understanding of LLM serving engines and distributed runtimes, including prefill vs. decode disaggregation, KV-cache management, and dynamic batching.
Expert-level Python programming skills with extensive experience designing custom test automation frameworks, diagnostic tooling, and CI/CD integration.
Strong proficiency with container orchestration tools (e.g., Kubernetes, Slurm, Ray) and high-performance cluster interconnects (e.g., InfiniBand, RoCE, NCCL).
Proven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems.

Nice To Haves

Direct experience with either AMD (ROCm / HIP) or NVIDIA software stacks.
Experience building workload replay tools, ML evaluation pipelines, or MLPerf Inference benchmark suites.
Familiarity with low-level kernel profiling tools (PyTorch Profiler, NVTX, ROCm profilers) or C++

Responsibilities

Design and implement automated test automation frameworks, regression gates, and release qualification pipelines for the complete GPU inference stack—spanning custom API services, model-serving workers, container runtimes, serving engines, driver stacks, and firmware.
Benchmark and stress-test distributed LLM serving frameworks, focusing on prefill vs. decode worker performance, continuous batching, prefix caching, KV-cache efficiency, and tensor/expert parallelism.
Build automated workload replay and benchmarking tools to validate GPU performance models. Track critical serving metrics including Time-to-First-Token (TTFT), Inter-Token Latency (ITL), request throughput, tail latency (P99), and capacity efficiency.
Build validation infrastructure to ensure model accuracy, precision stability (FP16/FP8/quantization), determinism, and output correctness across software updates, kernel fusions, and hardware revisions.
Engineer chaos engineering and fault-injection suites to simulate node failures, inter-node network degradation, GPU memory leaks, driver/firmware mismatches, and automated recovery paths for multi-node GPU clusters.
Integrate automated test pipelines with telemetry tools (e.g., Prometheus, Grafana) to turn one-off investigations into repeatable engineering gates and continuous performance monitoring.