C

Cerebras

AI accelerator hardware replacing GPUs

Infrastructure Systems Engineer - Linux, Storage & Automation

Full-TimePosted on 10/19/2026
No salary listed
Mid
Sunnyvale, CA, USA
Hybrid
IT OperationsSystem Administration

About the job

Requirements
  • Strong hands-on experience administering and troubleshooting Linux systems.
  • Proficiency in Python and shell scripting, particularly Bash.
  • Practical experience with Ansible for automation and configuration management.
  • Experience with virtualization platforms such as KVM, VMware, or Proxmox.
  • Hands-on experience administering enterprise or distributed storage systems.
  • Working knowledge of block, file, and object storage concepts, with practical experience in protocols such as NFS, SMB, iSCSI, or S3.
  • Familiarity with Linux storage management, including disk configuration, RAID, LVM, filesystems, and mount management.
  • Ability to troubleshoot storage performance using metrics such as IOPS, throughput, and latency.
  • Working knowledge of TCP/IP, DNS, DHCP, routing, VLANs, and network troubleshooting.
  • Familiarity with Linux services, permissions, processes, package management, and system logs.
  • Experience using Git for version control and collaborative development.
  • Ability to own technical problems through resolution and communicate findings clearly.
Responsibilities
  • Deploy, configure, and maintain Linux servers across physical and virtual environments.
  • Deploy and support data storage systems, including block, file, and object storage.
  • Configure and troubleshoot storage connectivity, volumes, filesystems, permissions, and access from Linux hosts and virtual machines.
  • Monitor storage capacity, performance, and availability; investigate latency, throughput, and connectivity issues.
  • Develop Python and Bash scripts to automate provisioning, configuration, validation, and routine operational tasks.
  • Build and maintain Ansible playbooks and roles for consistent, repeatable infrastructure deployments.
  • Troubleshoot issues across operating systems, applications, networking, storage, and virtualization platforms.
  • Investigate logs, analyze system behavior, and resolve performance, availability, and configuration problems.
  • Manage virtual machines and troubleshoot hypervisor, virtual networking, and storage integration issues.
  • Support server provisioning, operating system installation, patching, upgrades, and configuration management.
  • Support data protection and recovery through snapshots, replication, backups, and restore validation.
  • Implement monitoring, health checks, and alerts to identify problems early.
  • Collaborate with engineering, networking, storage vendors, and data center operations teams to deploy and support infrastructure.
  • Document configurations, troubleshooting procedures, and operational runbooks.
Desired Qualifications
  • Experience with storage platforms such as VAST Data, NetApp, Dell PowerScale, Pure Storage, or Ceph.
  • Familiarity with high-performance storage networking, including RDMA, RoCE, or InfiniBand.
  • Experience with bare-metal provisioning, PXE boot, Kickstart, or cloud-init.
  • Familiarity with server management interfaces such as BMC, IPMI, or Redfish.
  • Experience with Docker, Kubernetes, or other container technologies.
  • Familiarity with infrastructure monitoring and centralized logging tools.
  • Experience supporting data center, AI/ML, or high-performance computing environments.
  • Experience planning storage capacity, expanding storage systems, and supporting data migrations.

About the company

Cerebras Systems creates AI acceleration hardware and software. Its CS-2 system is designed to replace traditional GPU clusters for AI workloads, speeding up training and inference while simplifying the setup by eliminating the need for parallel programming, distributed training, and cluster management. The product works as a single, large processor-based accelerator with accompanying software and cloud services to run AI models efficiently, reducing latency and time to results. Compared with competitors, Cerebras differentiates itself with the largest processor in the industry and an integrated hardware-software stack that aims to streamline AI workflows rather than relying on multi-GPU clusters. The company's goal is to help research labs, healthcare, finance, and other industries achieve faster, more cost-effective AI development and deployment by offering a turnkey high-performance AI compute solution.

Company Size

501-1,000

Company Stage

IPO

Headquarters

Sunnyvale, California

Founded

2016

Get referred to Cerebras

See people who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • Q2 2026 core revenue hit $209.9 million, up 103% year over year.
  • Cerebras booked $25.4 billion in RPO on June 30, 2026.
  • Finland's September 1, 2026 Mikkeli campus adds 165MW contracted capacity.

What critics are saying

  • Data-center shortages force Cerebras to rent back customer systems, crushing near-term margins.
  • The August 2026 copyright class action in N.D. California targets model-training practices.
  • If power builds slip, Cerebras misses 2027 delivery and the backlog becomes stranded paper.

What makes Cerebras unique

  • Cerebras' wafer-scale chips still beat cluster-based AI systems for low-latency inference and training.
  • OpenAI signed a 750MW deal in December 2025, validating Cerebras' architecture.
  • AWS Bedrock integration and AMD disaggregation extend Cerebras beyond one-off hardware sales.

Help us improve and share your feedback! Did you find this helpful?

Benefits

  • Professional Development Budget
  • Flexible Work Hours
  • Remote Work Options
  • 401(k) Company Match
  • 401(k) Retirement Plan
  • Mental Health Support
  • Wellness Program
  • Paid Sick Leave
  • Paid Holidays
  • Paid Vacation
  • Parental Leave
  • Family Planning Benefits

Growth & Insights and Company News

6 month growth

-1%

1 year growth

-1%

2 year growth

2%
Yahoo Finance
Oct 12th, 2026
Cerebras, OpenAI's chip partner, bets on AI speed with a 750MW deal through 2028

Cerebras Systems (NASDAQ: CBRS), a lesser-known chipmaker, is drawing attention in the AI boom with a processor built for speed. Its Wafer-Scale Engine puts computing cores, memory, and communication on a single piece of silicon, cutting the need to move data between components. The company targets AI inference, where trained models answer questions, write code, and run tasks for millions of users. Inference is among the fastest-growing areas of the AI market. Cerebras and OpenAI agreed a multi-year deal to deploy 750 megawatts of Cerebras compute capacity, with staged deployment through 2028. In August, Cerebras said it powers the Ultrafast mode for OpenAI's GPT-5.6 Sol, which it says delivers up to 750 output tokens per second and runs up to 14 times faster than standard processing. Cerebras also announced its CS-4 system, which it says is up to twice as fast as its predecessor.

Yahoo Finance
Sep 30th, 2026
Cerebras secures $25.4B in signed work as cloud revenue jumps 103% despite data center bottleneck

Cerebras Systems holds $25.4 billion in signed contracts yet to be delivered, according to its June 30, 2026 figures. The AI hardware and cloud computing company projects core revenue of $880 million to $890 million for 2026, with expectations to more than triple in 2027. Second-quarter 2026 core revenue reached $209.9 million, up 103% year-over-year, driven primarily by cloud services including OpenAI deployment. The stock trades at 51.1 times sales, compared to 3.1 times for the S&P 500. Data centre capacity remains the key bottleneck. Cerebras has secured over 600 megawatts of capacity through end-2027. The company temporarily rents back some customer-operated systems to meet demand, pressuring gross margins short-term. Management expects AWS integration on Bedrock platform in first quarter 2027, with OpenAI remaining a significant but declining revenue portion.

TechCrunch
Sep 30th, 2026
Cerebras CEO Andrew Feldman to discuss AI scaling limits at TechCrunch Disrupt 2026

Cerebras Systems CEO Andrew Feldman will speak at TechCrunch Disrupt 2026 about AI scaling challenges. The company, which raised $5.5 billion in its May IPO, takes a different approach to AI computing through wafer-scale processors rather than conventional chip architectures. Cerebras recently signed a multiyear agreement with OpenAI to deploy 750 megawatts of systems from 2026 through 2028. The company has more than 600 megawatts of data centre capacity live or under contract for delivery by end of 2027 and is increasing manufacturing capacity more than tenfold during 2026. Feldman will discuss compute demand, energy and infrastructure constraints, and potential limits of current AI hardware. The event takes place 13-15 October at Moscone West in San Francisco.

Yahoo Finance
Sep 29th, 2026
BigBear.ai vs Cerebras Systems: Which AI stock is the better 2026 buy?

BigBear.ai and Cerebras Systems represent two distinct approaches to the AI market. BigBear.ai provides decision intelligence solutions primarily for US federal and defence agencies, using AI to help navigate supply chains, manage autonomous systems, and enhance cybersecurity. In FY 2025, BigBear.ai generated roughly $127.7 million in revenue, down approximately 19.3% year-over-year. The company reported a net loss of about $293.9 million, yielding a negative 230.2% net margin. Free cash flow was negative $46.3 million. Around 51% of total sales came from customers contributing over 10% of revenue individually. Cerebras Systems builds wafer-scale chips designed for demanding AI workloads. The company serves sectors including medical research, energy, and agentic AI through hardware sales and cloud services. BigBear.ai has 579 employees, many holding high-level security clearances, and maintains a debt-to-equity ratio of 0.0x with a current ratio of approximately 1.8x.

Yahoo Finance
Sep 28th, 2026
Gimlet Labs partners with Cerebras to deliver AI inference speeds up to 3,000 tokens per second

Gimlet Labs and Cerebras Systems announced a collaboration to deliver ultrafast AI inference through Gimlet Cloud. The partnership combines Cerebras' wafer-scale compute with Gimlet's inference technology to achieve speeds of up to 3,000 tokens per second for real-time AI applications. Gimlet Cloud integrates the Cerebras Wafer Scale Engine with GPUs, using advanced disaggregation technology to optimise each phase of inference. The first Cerebras-powered Gimlet Cloud datacentre is expected to launch later this year. The collaboration aims to improve user experience in real-time AI applications such as voice assistants and AI agents, where reduced latency creates more responsive interactions. Gimlet Labs will serve as a launch partner for Cerebras' CS-4 technology, providing developers with production-scale access to the combined infrastructure.