Performance Engineer

<div class="content-intro"><p><span data-contrast="none">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. </span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559685":0,"335559737":240,"335559738":240,"335559739":240,"335559740":279}"> </span></p> <p>Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. <a href="https://openai.com/index/cerebras-partnership/">OpenAI recently announced a multi-year partnership with Cerebras</a>, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. </p> <p>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.</p></div><div class="elementToProof"><strong>About The Role</strong></div> <div class="elementToProof">Join Cerebras as a Performance Engineer within our innovative Runtime Team. Our groundbreaking<span class="Apple-converted-space"> </span><span id="WEBEXT">CS-3</span>system, hosted by a distributed set of modern and powerful x86 machines, has set new benchmarks in high-performance ML training and inference solutions. It leverages a dinner-plate sized chip with 44GB of on-chip memory to surpass traditional hardware capabilities. This role will challenge and expand your expertise in optimizing AI applications and managing computational workloads primarily on the x86 architecture that run our Runtime driver.</div> <div class="elementToProof"> </div> <div class="elementToProof"><strong>Responsibilities</strong></div> <ul> <li>Focus on CPU and memory subsystem optimizations for our Runtime software driver, enabling faster key cloud and ML training/inference workloads across modern x86 machines that form the backbone of our AI accelerator.</li> <li>Develop and enhance algorithms for efficient data movement, local data processing, job submission, and synchronization between various software and hardware components.</li> <li>Optimize our workloads using advanced CPU features like AVX instructions, prefetch mechanisms, and cache optimization techniques.</li> <li>Perform performance profiling and characterization using tools such as AMD uprof, and reduce OS level overheads.</li> <li>Influence the design of Cerebras' next-generation AI architectures and software stack by analyzing the integration of advanced CPU features and their impact on system performance and computational efficiency.</li> <li>Engage directly with the AI and ML developer community to understand their needs and solve contemporary challenges with innovative solutions.</li> <li>Collaborate with multiple teams within Cerebras, including architecture, research, and product management, to elevate our computational platform and influence future designs.</li> </ul> <div class="elementToProof"><strong>Skills Qualifications</strong></div> <ul> <li>BS, MS, or PhD in Computer Science, Computer Engineering, or a related field.</li> <li>5+ years of relevant experience in performance engineering, particularly in optimizing algorithms and software design.</li> <li>Strong proficiency in C/C++ and familiarity with Python or other scripting languages.</li> <li>Demonstrated experience with memory subsystem optimizations and system-level performance tuning.</li> <li>Experien ... (truncated, view full listing at source)