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; 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.

Participate in bring-up of next-generation AI hardware systems and supporting software infrastructure. Debug complex system-level issues spanning hardware and software interactions. 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 recent hardware generations as systems move toward production readiness. Continuously improve internal engineering workflows related to debugging, testing, and automation. 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. 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.

Experience building automation frameworks, internal tooling, or test infrastructure 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 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.

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

📌 ML Systems Integration Engineer (Toronto)
🏢 Cerebras
📍 Toronto