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채용Cerebras

Engineering Manager, Kernel Reliability

Cerebras

Engineering Manager, Kernel Reliability

Cerebras

Sunnyvale CA or Toronto Canada

·

On-site

·

Full-time

·

2mo ago

필수 스킬

Parallel programming

Distributed systems

Debugging

Diagnostic tools

Leadership

Incident response

Computer architecture

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.

The Role

We're looking for a deeply technical, hands-on engineering leader for our on-field Kernel Reliability team. You will lead a high performing team to tackle a critical challenge: improving the reliability of our advanced compute clusters and the underlying inference, training, and internal production services. In this role, you'll set the technical vision while staying close to the code and designing solutions that will scale to our exponentially growing system production and software service offerings. If you have proven expertise in software or hardware reliability, diagnostic tool building, or failure analysis and debugging, we want to hear from you.

Responsibilities

  • Provide hands-on technical leadership, owning the technical vision and roadmap for the kernel-centric reliability of our internal and customer-facing systems

  • Assist System and Cluster Operations teams on reducing system and service downtime after failure by providing tooling and manual intervention for failure analysis and diagnostic

  • Work with the Debug Team to enhance debug tools with the goal of speeding up failure analysis Collaborate with SW teams to improve the software stack, including Kernels, to improve on-field debugging and failure analysis

  • Work with the ASIC an HW architecture teams to codesign the next generation architectures with reliability and ease of debug in mind

  • Lead, mentor, and grow a high-caliber team of engineers, fostering a culture of technical excellence and rapid execution.

Skills & Qualifications

  • 6+ years in software engineering, with 3+ years leading teams in SW/HW reliability, debug, diagnostic, failure analysis or related fields

  • Expertise in parallel and distributed programming (message passing, multicore, GPU, embeded, etc.), debug and diagnostic tool development or expert usage (debuggers, core dump handling, code sanitizers, etc.), experience debugging distributed and parallel applications (deadlocks, livelocks, race conditions, etc.), deep understanding of computer architectures (instruction pipelining, multithreading, networking, etc.)

  • Operations & Monitoring: Strong background in monitoring and reliability engineering (incident response, post-mortem analysis, etc.)

  • Leadership & Collaboration: Demonstrated ability to recruit and retain high-performing teams, mentor engineers, and partner cross-functionally to deliver customer-facing products.

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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Cerebras 소개

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

직원 수

Sunnyvale

본사 위치

$4.1B

기업 가치

리뷰

4.0

10개 리뷰

워라밸

2.8

보상

4.2

문화

4.1

커리어

4.3

경영진

3.5

72%

친구에게 추천

장점

Innovative and cutting-edge technology

Supportive and collaborative team environment

Good compensation and benefits

단점

Work-life balance challenges

High workload and expectations

Fast-paced and stressful environment

연봉 정보

33개 데이터

Mid/L4

Mid/L4 · Customer Solutions Architect

1개 리포트

$192,007

총 연봉

기본급

$166,962

주식

-

보너스

-

$192,007

$192,007

면접 경험

50개 면접

난이도

3.9

/ 5

소요 기간

21-35주

합격률

23%

경험

긍정 72%

보통 9%

부정 19%

면접 과정

1

Recruiter Screen

2

ML Coding

3

ML System Design

4

Research Discussion

5

Team Interviews

자주 나오는 질문

ML fundamentals

Design an ML system

Research paper discussion

Statistical concepts