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Added 1 min ago
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Cerebras

AI Fleet Platform Software Engineer

Toronto, ON, Canada

Job type: Full-time
Apply by: Nov 1, 2026
Experience: 12+ years of industry experience building and operating production software for distributed systems or large-scale infrastructure.
Posting language: English
Posted: Oct 2, 2026

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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.

The Role

Cerebras operates AI clusters across a growing number of data centers.

As the fleet expands, the software used to monitor and manage it must scale with it.

We are seeking a senior software engineer to build the platforms and tools that help our teams understand fleet health, respond to issues, and keep compute capacity available.

You will own substantial engineering work across backend services, operational tools, and user-facing applications.

You will partner with Cluster Operations, infrastructure, inference, security, and data center teams to turn real operational challenges into software that works reliably at scale.

Responsibilities

Build and operate software for managing large fleets of AI clusters
Give operators clear, actionable views of cluster health, capacity, performance, and ongoing issues.
Develop services and integrations that bring together data and workflows from multiple infrastructure systems.
Automate repetitive work and improve the tools teams use to investigate incidents and restore service.
Design systems that remain reliable as the fleet grows and continue to function through component and site failures.
Work closely with users of the platform to understand their needs and make practical product and engineering decisions.
Lead projects from initial design to production, measure their impact, and use operational feedback to guide improvements.

Skills And Requirements

12+ years of industry experience building and operating production software for distributed systems or large-scale infrastructure.
Strong Go or Python skills and experience designing services and APIs.
Expertise in control planes, fleet management systems, or operational platforms.
Experience with Linux, containers, Kubernetes, and handling failures across distributed systems.
Experience designing systems that handle asynchronous work, retries, and partial failures.
Experience with event streaming, workflow automation, or time-series telemetry.
Strong judgment in reliability, security, and observability.
Ability to lead ambiguous projects and work effectively across engineering and operations teams.

Preferred Experience

Building software for incident response, hardware health, or capacity management.
Developing dashboards and applications for infrastructure operators.
Working with AI clusters and their compute, networking, and hardware systems.

Location

SF Bay Area.
Toronto, Canada.

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

Build and operate software platforms, services, and tools for monitoring and managing large AI cluster fleets, including clear views of health, capacity, performance, and incidents. Lead projects from design through production, automate operational workflows, improve incident response and service restoration, and use feedback from operators and partner teams to guide improvements.

Requirements

Requires 12 or more years of experience building and operating production software for distributed systems or large-scale infrastructure, with strong Go or Python skills and expertise in control planes, fleet management, or operational platforms. Candidates should have experience with Linux, containers, Kubernetes, asynchronous and failure-prone systems, event streaming or telemetry, and the judgment and leadership to deliver reliable, secure, observable systems across engineering and operations teams.

Skills

Go
Python
Distributed Systems
Service And API Design
Control Planes
Fleet Management
Linux
Containers
Kubernetes
Failure Handling
Asynchronous Workflows
Retries And Partial Failures
Event Streaming
Workflow Automation
Time-Series Telemetry
Reliability, Security, And Observability

Visa sponsorship

Not detected in the job text

Keywords

AI Clusters
Fleet Management
Distributed Systems
Large-Scale Infrastructure
Go
Python
Backend Services
APIs
Control Planes
Operational Platforms
Linux
Containers
Kubernetes
Asynchronous Work
Retries
Partial Failures
Event Streaming
Workflow Automation
Time-Series Telemetry
Incident Response
Hardware Health
Capacity Management
Dashboards
Cluster Operations
Inference
Reliability
Security
Observability
Data Centers
Semiconductor Manufacturing

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