About the job
At Cerebras Systems, we are at the forefront of AI technology, developing the world's largest AI chip that is 56 times larger than conventional GPUs. Our innovative wafer-scale architecture enables the computational power of dozens of GPUs on a single chip, simplifying programming to the ease of handling one device. This unique design allows us to achieve unparalleled training and inference speeds, empowering machine learning practitioners to seamlessly deploy large-scale ML applications without the complexity of managing numerous GPUs or TPUs.
Our clientele includes leading model labs, global enterprises, and pioneering AI-native startups. Recently, OpenAI announced a multi-year partnership with Cerebras aimed at leveraging 750 megawatts of scale to revolutionize critical workloads through ultra-high-speed inference.
Thanks to our groundbreaking wafer-scale architecture, Cerebras Inference delivers the fastest Generative AI inference solution globally, exceeding GPU-based hyperscale cloud inference services by over tenfold. This significant boost in speed is transforming the user experience of AI applications, facilitating real-time iteration and enhancing intelligence through added agentic computation.
About The Role
As an Applied Machine Learning Research Scientist at Cerebras, you will be instrumental in converting modern machine learning methodologies into scalable, high-performance systems. This position focuses on the intersection of modeling and systems, emphasizing the efficient execution of existing algorithms rather than merely publishing new ones. Your efforts will significantly influence the training, optimization, and deployment of large language models (LLMs) on one of the most sophisticated AI platforms in existence.
You will collaborate closely with fellow researchers and senior engineers to enhance workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. Your responsibilities will encompass building training pipelines, debugging complex system behaviors, improving model quality, and refining data and evaluation strategies. Your contributions will have a direct and meaningful impact on advancing our capabilities in AI.
