ML Systems Integration Engineer

Cerebras Systems•Toronto, ON

About The Position

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.

Requirements

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.

Nice To Haves

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

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.