GPU Inference SDET

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

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 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.
• 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.
• 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:
  Integr