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职位Cerebras

Inference Core Platform Benchmarking Engineer

Cerebras

Inference Core Platform Benchmarking Engineer

Cerebras

Toronto, Ontario, Canada

·

On-site

·

Full-time

·

1mo ago

必备技能

Machine Learning

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 top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

About The Role

The Inference Core Platform group is at the heart of Cerebras' mission to deliver the world’s fastest AI inference. Our team builds the foundational software and hardware infrastructure that powers low-latency, high-speed, high-throughput deployment on the Cerebras Wafer-Scale Engine (WSE). We are responsible for the full stack—from model compilation and scheduling down to custom hardware kernels and driver development.

The Platform Benchmarking team plays a pivotal role in shaping the performance and scalability of AI inference on one of the most advanced computing systems ever built. We drive the bring-up of core inference capabilities and deliver performance improvements at every stage of development – from early prototyping to production deployment.

We're looking for passionate engineers to join us in redefining the limits of AI inference. If you thrive on building systems that measure, analyze, and optimize performance at scale, this is your opportunity to make a transformative impact on the future of AI.

Scope of the team includes:

  • Core Inference Observability – Design and implement end-to-end telemetry systems across the software stack, providing deep visibility into inference performance and enabling rapid iteration before and after deployment.

  • Benchmarking Infrastructure – Architect, build, and scale the automation that generates, analyzes, and visualizes performance data used to inform business decisions across engineering and leadership.

  • Performance Analysis– Dive deep into system behavior, dissect performance bottlenecks, and deliver actionable insights that directly influence which features ship and how they evolve.

  • Feature Integration – Partner closely with Core Platform teams to define rigorous testing methodologies that validate inference features for peak performance.

Skills & Qualifications

  • Bachelor’s or Master’s degree in Computer Engineering, Systems Engineering, or a related field.

  • Proficiency in Python and/or C++ programming.

  • Proven experience in building and scaling automated infrastructure.

  • Strong background in throughput and performance optimization techniques, especially in complex, large-scale systems.

  • Excellent problem-solving skills and a strong analytical mindset.

  • Demonstrated ability to dive deep into new domains.

  • Ability to work in a fast-paced, ambiguous, and collaborative environment.

Preferred Skills & Qualifications

  • Familiarity with problem-solving at the intersection of hardware and software.

  • Hands-on experience with AI workloads and architectures is a plus.

Location

  • On-site or hybrid at our Toronto office

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:

  • Build a breakthrough AI platform beyond the constraints of the GPU.

  • Publish and open source their cutting-edge AI research.

  • Work on one of the fastest AI supercomputers in the world.

  • Enjoy job stability with startup vitality.

  • Our simple, non-corporate work culture that respects individual beliefs.

Read our blog: Five Reasons to Join Cerebras in 2026.

Apply today and become part of the forefront of groundbreaking advancements in 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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关于Cerebras

Cerebras

Cerebras

Series F+

Cerebras Systems Inc. is an American artificial intelligence (AI) company with offices in Sunnyvale, San Diego, Toronto, and Bangalore, India. Cerebras builds computer systems for complex AI deep learning applications.

201-500

员工数

Sunnyvale

总部位置

$4.1B

企业估值

评价

4.0

10条评价

工作生活平衡

2.8

薪酬

4.2

企业文化

4.1

职业发展

4.3

管理层

3.5

72%

推荐给朋友

优点

Innovative and cutting-edge technology

Supportive and collaborative team environment

Good compensation and benefits

缺点

Work-life balance challenges

High workload and expectations

Fast-paced and stressful environment

薪资范围

33个数据点

Mid/L4

Mid/L4 · Customer Solutions Architect

1份报告

$192,007

年薪总额

基本工资

$166,962

股票

-

奖金

-

$192,007

$192,007

面试经验

50次面试

难度

3.9

/ 5

时长

21-35周

录用率

23%

体验

正面 72%

中性 9%

负面 19%

面试流程

1

Recruiter Screen

2

ML Coding

3

ML System Design

4

Research Discussion

5

Team Interviews

常见问题

ML fundamentals

Design an ML system

Research paper discussion

Statistical concepts