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Cerebras

Staff GPU Inference SDET

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 9, 2026
Experience: 8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer.
Posting language: English
Posted: Sep 23, 2026

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Original job 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.

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.

About The Role

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.

What You’ll Do

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.

Requirements

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.

Nice To Haves

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

About

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.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!

Apply today and become part of the forefront of groundbreaking advancements in AI!

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.

This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.

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More about this job

Responsibilities

Build and scale automated release qualification, regression testing, and CI/CD systems for the GPU inference stack and multi-node GPU clusters. Validate serving performance, numerical correctness, and resilience through workload benchmarking, telemetry, fault injection, and failure analysis.

Requirements

Requires 8+ years of software engineering experience in SDET, infrastructure quality, or systems testing, along with hands-on experience provisioning and validating multi-node GPU clusters. Candidates should have expert Python automation skills, knowledge of LLM serving and distributed runtimes, proficiency with orchestration and high-performance networking, and experience debugging failures across software and hardware.

Skills

GPU Cluster Validation
LLM Inference Serving
Python
Test Automation
CI/CD
Kubernetes
Slurm
Ray
InfiniBand
RoCE
NCCL
Performance Benchmarking
Numerical Correctness
Fault Injection
Root-Cause Analysis
Prometheus and Grafana

Visa sponsorship

Not detected in the job text

Keywords

GPU Inference
Multi-Node GPU Clusters
NVIDIA
AMD
ROCm
HIP
LLM Serving
Prefill and Decode Disaggregation
KV Cache
Dynamic Batching
Tensor Parallelism
Expert Parallelism
Python
Test Automation Frameworks
CI/CD
Kubernetes
Slurm
Ray
InfiniBand
RoCE
NCCL
Prometheus
Grafana
MLPerf Inference
Workload Replay
FP16
FP8
Quantization
Fault Injection
PyTorch Profiler

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