Cerebras' systems are designed with a focus on Machine Learning and High Performance Computing. Our processor is the Wafer Scale Engine (WSE), a single chip with performance equivalent to a cluster of GPUs, giving the user cluster-scale capability with the simplicity of programming a single device. Because of this programming simplicity, large model training can be scaled out using simple data parallelism to the performance of thousands of GPUs. ML practitioners can focus on their machine learning, rather than parallelizing and distributing their applications across many devices. The Cerebras hardware architecture is designed with unique capabilities including orders of magnitude higher memory bandwidth and unstructured sparsity acceleration, not accessible on traditional GPUs. With a rare combination of cutting-edge hardware and deep expertise in machine learning, we stand among the select few global organizations capable of conducting large-scale innovative deep learning research and developing novel ML algorithms not possible on traditional hardware.

About the role

We are looking for a senior ML frameworks engineer who can work with a team of talented engineers to develop innovative solutions to integrating machine learning frameworks, such as PyTorch, with our deeply vertical software stack. You will be responsible for architecting solutions for taking arbitrary user models in their ML framework of choice and making them run on our hardware with high speed, while presenting an easy to use APIs. You will also collaborate with other teams to integrate machine learning capabilities into our products and services.

Responsibilities

• Provide technical guidance and direction to a team of machine learning engineers working on various machine learning projects.
• Design and implement scalable and efficient integration with popular machine learning frameworks such as PyTorch.
• Understand characteristics of various ML models to make good design choices when it comes to designing scalable and easy-to-use APIs.
• Research and apply the latest advancements in machine learning and deep learning to improve our solutions.
• Evaluate trade-offs between various approaches, articulate design choices clearly, and suggest proposals for designing new features.
• Work in an agile team to deliver high quality software.
• Collaborate with other teams to integrate machine learning features into our products and services.

Requirements

• Bachelor’s degree, Masters degree, or PhD in computer science, computer engineering, mathematics, or related field.
• 4+ years of experience in large scale software engineering, preferably related to deep learning.
• Proficient in Python for developing large-scale systems.
• Proficient in C++, with focus on multi-threaded programming and networking.
• Experience with PyTorch, TensorFlow, JAX, or other machine learning frameworks and libraries.
• Deep understanding of architectural patterns of large scale software applications.
• Experience in leading and mentoring machine learning engineers.
• Strong knowledge of machine learning theory, algorithms, and software engineering best practices.
• Ability to work independently and collaboratively with cross-functional teams.
• Excellent communication, presentation, and problem-solving skills.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU
2. Publish and open source their cutting-edge AI research
3. Work on one of the fastest AI supercomputers in the world
4. Enjoy job stability with startup vitality
5. Our simple, non-corporate work culture that respects individual beliefs

Read our blog: Five Reasons to Join Cerebras in 2024.