Responsibilities

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.
Build GPU Release Qualification Systems:
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.
Inference Serving & Workload Validation:
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.
Performance & Performance Modeling Verification:
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.
Numerical Correctness & Quality Gates:
Build validation infrastructure to ensure model accuracy, precision stability (FP16/FP8/quantization), determinism, and output correctness across software updates, kernel fusions, and hardware revisions.
Fault Injection & Fleet Resilience:
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.
Observability & CI/CD Integration:

Qualification

GPU & Cluster Infrastructure ExpertiseHands-on experience bringing upInference Stack KnowledgeAutomation & ScriptingOrchestration & NetworkingFailure Analysis & DebuggingWhy Join Cerebras

Required

8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer.
GPU & Cluster Infrastructure Expertise:
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.
Inference Stack Knowledge:
Deep understanding of LLM serving engines and distributed runtimes, including prefill vs. decode disaggregation, KV-cache management, and dynamic batching.
Automation & Scripting:
Expert-level Python programming skills with extensive experience designing custom test automation frameworks, diagnostic tooling, and CI/CD integration.
Orchestration & Networking:
Strong proficiency with container orchestration tools (e.g., Kubernetes, Slurm, Ray) and high-performance cluster interconnects (e.g., InfiniBand, RoCE, NCCL).
Failure Analysis & Debugging:
Proven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems.

Preferred

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++
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.

Staff GPU Inference SDET at Cerebras · LumiJobs

Staff GPU Inference SDET

Responsibilities

Qualification

Required

Preferred

Company

Cerebras

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