ML Compiler Stack Engineer

Posted 12 Days Ago
Be an Early Applicant
Toronto, ON
Entry level
Artificial Intelligence
The Role
As a Compiler Engineer at Cerebras Systems, you will design and optimize compiler technologies for AI chips using LLVM and MLIR frameworks. You will work closely with the machine learning team to enhance compiler performance, address bottlenecks, and ensure efficient resource utilization for AI applications.
Summary Generated by Built In

Cerebras Systems builds the world’s largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

 

Cerebras’ current customers include national labs, global corporations across multiple industries, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields.

 

Job Overview:


We are seeking a highly skilled Compiler Engineer with a passion of optimizing compiler technologies for AI workloads. You will be an integral part of our software compiler stack team, focusing on enhancing our compiler to fully leverage the unique capabilities of our CS3 system. Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

 

Key Responsibilities:

  • Design, develop, and optimize compiler technologies for AI chips using LLVM and MLIR frameworks.
  • Identify and address performance bottlenecks, ensuring optimal resource utilization and execution efficiency.
  • Work with the machine learning team to integrate compiler optimizations with AI frameworks and applications.
  • Contribute to the advancement of compiler technologies by exploring new ideas and approaches.

 

Qualifications:

  • Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, or a related field.
  • Proven experience in compiler development, particularly with LLVM and/or MLIR.
  • Strong background in optimization techniques, particularly those involving NP-hard problems.
  • Proficiency in C/C++ programming and experience with low-level optimization.
  • Familiarity with AI workloads and architectures is a plus.
  • Excellent problem-solving skills and a strong analytical mindset.
  • Ability to work in a fast-paced, collaborative environment.

 

What We Offer:

  • Competitive salary and benefits package.
  • Opportunities for professional growth and career advancement.
  • A dynamic and innovative work environment.
  • The chance to work on cutting-edge technologies and make a significant impact on the future of AI.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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Top Skills

C
C++
The Company
HQ: Sunnyvale, CA
402 Employees
On-site Workplace
Year Founded: 2016

What We Do

Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.

The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.

Join us: https://cerebras.net/careers/

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