Senior ML Systems Engineer
AI summary of the role
This role is on the SOTA Training Platform team at Cerebras, responsible for rapidly bringing up state-of-the-art open-source and proprietary ML models on the Cerebras CSX wafer-scale systems.
What you’ll do
- 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.
What you’ll bring
- 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).
- 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.
Technologies
PyTorch · TensorFlow · C/C++ · LLVM · MLIR · compiler IR · performance profiling · MoE · diffusion · reinforcement learning
About Cerebras Systems
Builds wafer-scale AI processors, CS-3 systems, and cloud inference services that deliver ultra-fast training and inference without conventional multi-GPU orchestration overhead.
Growth
Source and classification
Internal deployment & tooling · Evidence for this classification:
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
More from the 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 [... source excerpt omitted ...] 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. Study emerging training and post-training algori
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