Full-Time

Performance Engineer

Confirmed live in the last 24 hours

Cerebras

Cerebras

201-500 employees

Develops AI acceleration hardware and software

No salary listed

Mid, Senior

Toronto, ON, Canada

Category
Applied Machine Learning
Computer Vision
AI & Machine Learning
Required Skills
Python
Machine Learning
C/C++
Requirements
  • Masters in Electrical Engineering or Computer Science.
  • Strong background in computer architecture.
  • Strong analytical and problem solving mindset.
  • 3+ years of experience in a relevant domain (Computer Architecture, Network Performance, CPU/GPU Performance, Kernel Optimization, HPC).
  • Experience working on CPU/GPU simulators.
  • Exposure to performance profiling and debug on any system pipeline.
  • Comfort with C++ and Python.
Responsibilities
  • Build performance models to understand the project the performance of state of the art and customer models.
  • Optimize our Kernel micro code and Compiler algorithms to elevate ML model utilization on the Cerebras WSE.
  • Debug and understand runtime performance on the system and cluster.
  • Design performance features for upcoming ML architectures to enable highest performance execution on both training and inference.
  • Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.
Desired Qualifications
  • Exposure to and basic understanding of machine learning is desired.
  • Any pre-silicon performance validation exposure is a plus but not necessary.

Cerebras Systems specializes in accelerating artificial intelligence (AI) processes with its CS-2 system, which is designed to replace traditional clusters of graphics processing units (GPUs) used in AI computations. The CS-2 system simplifies the complexities of parallel programming, distributed training, and cluster management, making AI tasks more efficient. Clients from various sectors, including pharmaceuticals, government research labs, healthcare, finance, and energy, benefit from the system's ability to deliver faster results, which is essential for critical applications like cancer drug response predictions. Cerebras generates revenue by selling its proprietary hardware and software solutions, including the CS-2 systems and related cloud services. The company's goal is to provide a comprehensive solution that enables clients to achieve quicker AI training and lower latency in AI inference, ultimately reducing the costs associated with AI research and development.

Company Size

201-500

Company Stage

Series F

Total Funding

$700.4M

Headquarters

Sunnyvale, California

Founded

2016

Simplify Jobs

Simplify's Take

What believers are saying

  • Growing AI model efficiency demand aligns with Cerebras' energy-efficient accelerators.
  • AI democratization increases need for user-friendly systems like Cerebras' CS-2.
  • Pharmaceutical industry's push for faster drug discovery boosts demand for Cerebras' technology.

What critics are saying

  • Competition from NVIDIA and Graphcore could impact Cerebras' market share.
  • Rapid AI model evolution may necessitate frequent hardware updates, increasing R&D costs.
  • Supply chain vulnerabilities could delay production of Cerebras' hardware.

What makes Cerebras unique

  • Cerebras' Wafer-Scale Engine is the largest chip ever built for AI.
  • The CS-2 system replaces traditional GPU clusters, simplifying AI computations.
  • Cerebras serves diverse industries, including pharmaceuticals and government research labs.

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Benefits

Professional Development Budget

Flexible Work Hours

Remote Work Options

401(k) Company Match

401(k) Retirement Plan

Mental Health Support

Wellness Program

Paid Sick Leave

Paid Holidays

Paid Vacation

Parental Leave

Family Planning Benefits

Fertility Treatment Support

Adoption Assistance

Childcare Support

Elder Care Support

Pet Insurance

Bereavement Leave

Employee Discounts

Company Social Events