AI Infrastructure Operations Engineer

Posted 4 Hours Ago
Be an Early Applicant
2 Locations
Remote
Entry level
Artificial Intelligence • Hardware • Software • Semiconductor
The Role
Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.
Summary Generated by Built In

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.

About The Role

The AI Infrastructure Operations Engineer (SiteOps) is an entry-level individual contributor role focused on the deployment, bring-up, monitoring, and first-line troubleshooting of Cerebras AI infrastructure in data center environments. The role supports CS systems, cluster server hardware, cluster networking hardware, and hardware telemetry and monitoring tools.

Support reliable operation and scale-out of Cerebras AI clusters by executing defined hardware bring-up and validation procedures, monitoring telemetry, performing first-line troubleshooting, and escalating issues using established workflows.

Responsibilities

  • Assist with deployment and bring-up of CS-X systems, cluster servers, and networking hardware;
    • Execute power-on sequencing, readiness checks, and validation tests.
    • Monitor hardware telemetry, alerts, and dashboards.
    • Perform first-line troubleshooting and structured escalation.
    • Collect logs, telemetry, and observations during incidents.

Incident Support & Tooling

  • Participate in incident response under senior engineer guidance;
    • Use existing monitoring, telemetry, and incident tracking tools.
    • Provide feedback on tooling and process gaps.

Learning & Development

  • Build working knowledge of Cerebras system architecture;
    • Learn cluster hardware and networking fundamentals.
    • Shadow senior engineers during complex debugging.
    • Progress toward independent ownership of defined workflows.

Explicit Non-Responsibilities

  • No people management;
    • No final escalation authority.
    • No ownership of cluster architecture, hardware design, or tooling architecture.

Required Qualifications

Bachelor’s degree in a relevant engineering field or equivalent experience; 0–3 years experience in hardware operations, systems engineering, or datacenter environments; basic familiarity with server hardware, networking fundamentals, and Linux systems.

Preferred Qualifications

Internship or early-career experience in datacenter or hardware lab environments; exposure to monitoring or telemetry systems; comfort working in data centers.

What Success Looks Like

Consistent and correct execution of hardware bring-up procedures, early identification and escalation of issues, improving documentation quality, and clear progression toward more independent operational responsibility.

Career Path

This role progresses naturally toward Senior and Principal IC roles within AI Infrastructure Operations (SiteOps), with an optional management track.

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:

  1. Build a breakthrough AI platform beyond the constraints of the GPU.

  2. Publish and open source their cutting-edge AI research.

  3. Work on one of the fastest AI supercomputers in the world.

  4. Enjoy job stability with startup vitality.

  5. 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.

Skills Required

  • Bachelor's degree in a relevant engineering field or equivalent experience
  • 0-3 years experience in hardware operations, systems engineering, or datacenter environments
  • Basic familiarity with server hardware, networking fundamentals
  • Basic familiarity with Linux systems
  • Internship or early-career experience in datacenter or hardware lab environments
  • Exposure to monitoring or telemetry systems
  • Comfort working in data centers
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The Company
774 Employees
Year Founded: 2015

What We Do

Cerebras Systems develops wafer-scale semiconductor hardware, AI supercomputers, and software/cloud services for training and inference. Its CS-2 and CS-3 systems help organizations build on-premise AI supercomputers, while pay-as-you-go cloud offerings provide developers and enterprises access to its computing platform. The company focuses on making AI training and inference faster and easier for diverse research and production workloads at scale worldwide.

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