Staff GPU Inference SDET

The role

You will be the founding quality, reliability, and validation lead for a new GPU Inference Development team. You will design, build, and scale the end-to-end release qualification and automated test ecosystem for the GPU inference stack and rack-scale accelerated compute fleets.

What you will do

• Design and implement automated test automation frameworks, regression gates, and release qualification pipelines.
• Benchmark and stress-test distributed LLM serving frameworks.
• Build automated workload replay and benchmarking tools to validate GPU performance models.
• Build validation infrastructure to ensure model accuracy, precision stability, determinism, and output correctness.
• Engineer chaos engineering and fault-injection suites to simulate node failures and network degradation.
• Integrate automated test pipelines with telemetry tools.

Required qualifications

• 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 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++

About the company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.