Staff 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.

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:

Integrate automated test pipelines with telemetry tools (e.g., Prometheus, Grafana) to turn one-off investigations into repeatable engineering gates and continuous performance monitoring.

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.

• 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++

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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