Staff GPU Inference SDET - Sunnyvale, CA, United States

Originally posted 9 September 2026 by the employer.

Build and scale the end-to-end release qualification and automated test ecosystem for a new GPU Inference Development team.

About the role

What you'll do

• 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, tracking 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 (Prometheus, Grafana) for continuous performance monitoring and repeatable engineering gates.

What you'll need

• 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 (Kubernetes, Slurm, Ray) and high-performance cluster interconnects (InfiniBand, RoCE, NCCL).
• Proven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems.

Nice to have

• 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++

Skills: GPU Inference, automated test ecosystem, LLM serving frameworks, container orchestration, multi-node GPU clusters, performance validation