Lead Full Stack Machine Learning Engineer

Responsibilities

• Contribute to the end-to-end bring up of frameworks for RL, inference serving, 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.

Requirements

• Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field with 10+ years' experience.
• Comfort navigating the full AI toolchain: Python modelling 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).
• Proficiency in C/C++ programming and experience with low-level optimization.
• Strong background in optimization techniques, particularly those involving NP-hard problems.

Benefits

Additional Information

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 T he Role This teams' principal responsibility is to rapidly bring up state-of-the-art open-source models, frameworks and data engineering. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire software stack. Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

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