About the role

As an applied machine learning engineer, you will work on adapting state of the art deep learning (DL) models to run on our wafer scale system. This includes both functional validation and performance tuning of a variety of core models for applications like Natural Language Processing (NLP), Large Language Models (LLMs), Computer Vision (CV) and Graph Neural Networks (GNN).

As a member of the Cerebras engineering team you will be implementing models in popular DL frameworks like PyTorch and using insights into our hardware architecture to unlock to full potential of our chip. You will work on all aspects of the DL model pipeline including:

• Dataloader implementation and performance optimization
• Reference model implementation and functional validation
• Model convergence and hyper-parameters tuning
• Model customization to meet customer needs.
• Model architecture pathfinding.

This role will allow you to work closely with partner companies at the forefront of their fields across many industries. 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

• Analyze, implement, and optimize DL models for the WSE
• Functional and convergence of models on the WSE
• Work with engineering teams to optimize models for the Cerebras stack
• Support engineering teams in functional and performance scoping new models and layers
• Work with customers to optimize their models for the Cerebras stack.
• Develop new approaches for solving real world AI problems on various domains.

Requirements

• Bachelor's degree in engineering, science, or related field with 8+ years of experience
• Experience programming in modern language like Python or C++
• In-depth understanding of DL learning methods and model architectures
• Experience with DL frameworks like PyTorch, TensorFlow and JAX
• Familiar with state-of-the-art transformer architectures for language and vision model.
• Experience in model training and hyper-parameter tuning techniques.
• Familiar with different LLM downstream tasks and datasets.

Preferred Skills

• A deep passion for cutting edge artificial intelligence techniques
• Master's or PhD in engineering, science or related field
• Understanding of hardware architecture
• Experience programming accelerators like GPUs and FPGAs