Senior Full Stack LLM Engineer - Training

Confirmed live in the last 24 hours

Cerebras Systems

Sunnyvale CA or Toronto Canada

On-site

Posted March 17, 2026

Job Description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras' current customers include top 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.

Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.

About the Role
We are seeking a versatile and experienced engineer to join our SOTA Training Platform team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

Responsibilities

Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.

Skills & Qualifications

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
5+ years of relevant industry experience (internship/co-op experience included)
Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
Strong debugging skills across performance, numerical accuracy, and runtime integration.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).

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