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Cerebras

ML Systems Integration Engineer

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 3, 2026
Posting language: English
Posted: Sep 23, 2026

Apply About employer

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Original job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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.

Cerebras works with the leading 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.

Responsibilities

Participate in bring-up of next-generation AI hardware systems and supporting software infrastructure.
Debug complex system-level issues spanning hardware and software interactions.
Investigate failures occurring during system bring-up and identify root causes using logs, telemetry, and diagnostic tools.
Build automation frameworks and internal tooling that improve system validation and debugging workflows.
Develop software used to test, validate, and stress distributed hardware systems during development and production cycles.
Collaborate closely with hardware engineers to isolate and resolve system integration issues.
Improve system observability by building tools that surface failures quickly and accelerate debugging.
Reproduce, triage, and diagnose difficult issues that arise during early hardware deployment.
Support validation and qualification of new hardware generations as systems move toward production readiness.
Continuously improve internal engineering workflows related to debugging, testing, and automation.

Skills & Qualifications

BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or related technical field.
Strong programming skills in Python and/or C++.
Excellent debugging and problem-solving skills with ability to investigate complex technical issues methodically.
Solid understanding of operating systems fundamentals (processes, threads, memory management, concurrency, IPC).
Experience working in Linux development environments.
Understanding of computer architecture and interactions between hardware and software systems.
Strong analytical thinking and ability to break down complex system failures into actionable root causes.
Ability to work effectively across multiple engineering teams and collaborate in highly technical environments.
Strong communication skills and willingness to work on ambiguous technical problems.

Preferred Skills & Qualifications

Experience building automation frameworks, internal tooling, or test infrastructure
Familiarity with distributed systems concepts
Experience debugging large-scale systems or complex infrastructure environments
Understanding of networking fundamentals and communication between distributed systems
Experience working with hardware-adjacent software or system integration environments
Familiarity with performance analysis, system telemetry, and log analysis
Exposure to production systems validation or infrastructure reliability engineering

Why Join Cerebras

About

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.

Find out more about what it's like to work at Cerebras here!

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.

This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.

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More about this job

Responsibilities

Bring up and validate next-generation AI hardware systems, diagnose complex hardware-software integration failures, and collaborate with hardware engineers to resolve system issues. Build automation, testing, and observability tools that improve debugging and validation workflows through development and production readiness.

Requirements

A bachelor's or master's degree in computer science, computer engineering, electrical engineering, or a related technical field is required, along with strong Python and/or C++ programming skills. Candidates should have Linux experience, knowledge of operating systems and computer architecture, and strong analytical, debugging, and collaboration skills; experience with automation, distributed systems, networking, or system validation is preferred.

Skills

Python
C++
Linux
Operating Systems
Debugging
Computer Architecture
Hardware-Software Integration
Automation Frameworks
Test Infrastructure
Distributed Systems
Networking
System Telemetry
Log Analysis
Performance Analysis
System Validation
Concurrency

Visa sponsorship

Not detected in the job text

Keywords

Cerebras Systems
AI Hardware
AI Systems
Python
C++
Linux
Operating Systems
Processes
Threads
Memory Management
Concurrency
Interprocess Communication
Computer Architecture
Hardware-Software Integration
System Bring-Up
System Debugging
Root Cause Analysis
Automation Frameworks
Internal Tooling
Test Infrastructure
Distributed Systems
Networking
System Telemetry
Log Analysis
Performance Analysis
System Validation
Hardware Qualification
Infrastructure Reliability
Production Readiness

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