{"id":585628,"url":"https://alion.io/job/cerebras-systems-staff-gpu-inference-sdet","title":"Staff GPU Inference SDET","company":{"id":31,"name":"Cerebras Systems","domain":"cerebras.ai","url":"https://alion.io/company/cerebras-systems","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":87,"open_postings":113,"ghost_share":0.142,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":97,"computed_at":"2026-11-01T06:10:25Z"}},"role":"QA","role_family":"QA","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States","Toronto, Canada"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":184000,"max_usd":371000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":160},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Cerebras","optional":false},{"name":"Chaos Engineering","optional":false},{"name":"CI/CD","optional":false},{"name":"Grafana","optional":false},{"name":"InfiniBand","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"NCCL","optional":false},{"name":"OpenAI","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"SLURM","optional":false},{"name":"C++","optional":true},{"name":"PyTorch","optional":true},{"name":"ROCm","optional":true}],"status":"live","first_seen_at":"2026-09-09T22:33:33Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-11-02T05:51:59Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":53},"description":"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. \nThis 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.\nCerebras 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.\nAbout the Role\nAs 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.\nWHAT YOU’LL DO\nBuild GPU Release Qualification Systems:\nDesign 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.\nInference Serving & Workload Validation:\nBenchmark 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.\nPerformance & Performance Modeling Verification:\nBuild 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.\nNumerical Correctness & Quality Gates:\nBuild validation infrastructure to ensure model accuracy, precision stability (FP16/FP8/quantization), determinism, and output correctness across software updates, kernel fusions, and hardware revisions.\nFault Injection & Fleet Resilience:\nEngineer 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.\nObservability & CI/CD Integration:\nIntegrate automated test pipelines with telemetry tools (e.g., Prometheus, Grafana) to turn one-off investigations into repeatable engineering gates and continuous performance monitoring.\nREQUIREMENTS:\n8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer.\nGPU & Cluster Infrastructure Expertise:\nHands-on experience bringing up, provisioning, and validating multi-node GPU clusters (NVIDIA or AMD ecosystem) across public cloud infrastructure or enterprise data center environments.\nInference Stack Knowledge:\nDeep understanding of LLM serving engines and distributed runtimes, including prefill vs. decode disaggregation, KV-cache management, and dynamic batching.\nAutomation & Scripting:\nExpert-level Python programming skills with extensive experience designing custom test automation frameworks, diagnostic tooling, and CI/CD integration.\nOrchestration & Networking:\nStrong proficiency with container orchestration tools (e.g., Kubernetes, Slurm, Ray) and high-performance cluster interconnects (e.g., InfiniBand, RoCE, NCCL).\nFailure Analysis & Debugging:\nProven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems.\nNICE TO HAVES:\nDirect experience with either AMD (ROCm / HIP) or NVIDIA software stacks.\n\nExperience building workload replay tools, ML evaluation pipelines, or MLPerf Inference benchmark suites.\n\nFamiliarity with low-level kernel profiling tools (PyTorch Profiler, NVTX, ROCm profilers) or C++\n\nWhy Join Cerebras\nPeople 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:\nBuild a breakthrough AI platform beyond the constraints of the GPU.\n\nPublish and open source their cutting-edge AI research.\n\nWork on one of the fastest AI supercomputers in the world.\n\nEnjoy job stability with startup vitality.\n\nOur simple, non-corporate work culture that respects individual beliefs.\n\nFind out more about what it's like to work at Cerebras here!\nApply today and become part of the forefront of groundbreaking advancements in AI!\nCerebras 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.\nThis website or its third-party tools process personal data. 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