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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. 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.
Job Responsibility:
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
Requirements:
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)
Proficiency in C/C++ programming and experience with low-level optimization
Proven experience in compiler development, particularly with LLVM and/or MLIR
Strong background in optimization techniques, particularly those involving NP-hard problems
What we offer:
Competitive salary and benefits package
Opportunities for professional growth and career advancement
A dynamic and innovative work environment
The chance to work on cutting-edge technologies and make a significant impact on the future of AI