Cerebras has developed a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.

We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for deep learning workloads.

About the role

As an applied machine learning engineer, you will take today’s state-of-the-art solutions in various verticals and adapt them to run on the new Cerebras system architecture. You will get to see how deep learning is being applied to some of the world’s most difficult problems today and help ML researchers in these fields to innovate more rapidly and in ways that are not currently possible on other hardware systems.

Responsibilities

• Familiar with state-of-the-art transformer architectures for language and vision model.
• Bring up new state-of-the art model on Cerebras System and function validation.
• Train a model to convergence, and hyper-parameter tuning.
• Optimize model code to run efficiently on Cerebras System.
• Explore new model architecture that take advantage of Cerebras unique capabilities.
• Develop new approaches for solving real world AI problems on various domains.

Requirements

• BS or Masters in Computer Science or related field
• Familiarity with JAX/TensorFlow/PyTorch
• Good understanding of how to define custom layers and back-propagate through them.
• Experience with transformer deep learning models
• Experience in vertical such as computer vision or language modeling
• Experience with Large Language Models such as GPT family, Llama, BLooM.