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Cerebras

AI accelerator hardware replacing GPUs

ML Systems Performance Engineer

Systems Software EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedHybrid

About the Job

Requirements

  • A Bachelor's, Master's, or PhD in Electrical Engineering or Computer Science is required.
  • A strong background in computer architecture is required.
  • Exposure to and understanding of low-level deep learning and large language model mathematics is required.
  • At least 3 years of experience in a relevant domain, such as computer architecture, CPU/GPU performance, kernel optimization, or high-performance computing, is required.
  • Experience working on CPU/GPU simulators is required.
  • Exposure to performance profiling and debugging on a system pipeline is required.
  • Comfort with C++ and Python is required.

Responsibilities

  • Build kernel-level and end-to-end performance models to estimate the performance of state-of-the-art and customer machine learning models.
  • Optimize and debug kernel microcode and compiler algorithms to improve machine learning model inference speed, throughput, and compute utilization on the Cerebras Wafer Scale Engine.
  • Debug and understand runtime performance on the system and cluster.
  • Develop tools and infrastructure to visualize performance data collected from the Wafer Scale Engine and compute cluster.

About the Company

Cerebras

AI accelerator hardware replacing GPUs

Cerebras Systems creates AI acceleration hardware and software. Its CS-2 system is designed to replace traditional GPU clusters for AI workloads, speeding up training and inference while simplifying the setup by eliminating the need for parallel programming, distributed training, and cluster management. The product works as a single, large processor-based accelerator with accompanying software and cloud services to run AI models efficiently, reducing latency and time to results. Compared with competitors, Cerebras differentiates itself with the largest processor in the industry and an integrated hardware-software stack that aims to streamline AI workflows rather than relying on multi-GPU clusters. The company's goal is to help research labs, healthcare, finance, and other industries achieve faster, more cost-effective AI development and deployment by offering a turnkey high-performance AI compute solution.

Company size
501-1,000
Company stage
IPO
Headquarters
Sunnyvale, CA
Founded
2016

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