About the job
Cerebras Systems is revolutionizing the AI landscape with the world’s largest AI chip, which is 56 times larger than traditional GPUs. Our innovative wafer-scale architecture offers AI compute power that surpasses dozens of GPUs, all on a single chip, while ensuring the programming ease of a single device. This cutting-edge technology enables Cerebras to achieve unparalleled training and inference speeds, allowing machine learning enthusiasts to seamlessly execute large-scale ML applications without the complexity of managing multiple GPUs or TPUs.
Cerebras is trusted by leading model labs, global corporations, and pioneering AI-native startups. Notably, OpenAI has recently formed a multi-year partnership with Cerebras, committing to deploy 750 megawatts of scale and transforming critical workloads through ultra-high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference delivers the fastest Generative AI inference solution globally, outpacing GPU-based hyperscale cloud inference services by over tenfold. This significant speed enhancement is redefining the user experience of AI applications, facilitating real-time iteration and amplifying intelligence via enhanced agentic computation.
About The Role
As an Applied AI Scientist within the FieldML team, your role will involve developing and personalizing large language models and broader large-scale deep learning models tailored to address specific customer challenges. You won’t merely provide guidance; you will construct solutions. Your efforts will help bridge the gap between state-of-the-art research and practical applications by empowering customers to exploit the potential of the Cerebras Wafer-Scale Engine (WSE) in their AI projects.
We seek experienced AI Scientists who are not just passionate about machine learning but are also excited to implement, train, and scale models to tackle intricate business and scientific problems. You will engage in a wide array of projects, from training custom models from the ground up to fine-tuning and optimizing the latest Large Language Models.
