---
title: 'Compute Server Platform Architect at Cerebras Systems'
canonical: 'https://feeny.ai/job/compute-server-platform-architect-cerebras-systems-united-states-and-afzxyzr474n9'
type: 'job'
last_seen: '2026-09-15'
---

# Compute Server Platform Architect at Cerebras Systems

- **Company:** [Cerebras Systems](https://feeny.ai/companies/cerebras-systems)
- **Location:** United States and, Canada
- **Employment:** full-time
- **Posted:** 2026-02-18
- **Last confirmed live:** 2026-09-15
- **Apply:** https://jobs.ashbyhq.com/cerebras/9f6c08a7-66c5-4b54-9951-07b2d33b3beb/application

**Skills:** x86 server architecture, Linux systems, RDMA, RoCE, NVMe, C, C++, Python, Performance modeling, Benchmarking

> Own the server-side platform architecture for Cerebras AI clusters, defining hardware requirements for CPU, memory, IO, and networking. Drive vendor evaluations, performance modeling, and qualification to ensure predictable performance, scalability, and reliability for training and inference workloads.

## 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](https://openai.com/index/cerebras-partnership/) with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

## About The Role

As a Compute / Server Platform Architect on the Cluster Architecture Team, you will own the server-side platform architecture that enables Cerebras CS3-based AI clusters (training and inference) to deliver predictable performance, scalability, and reliability. Our accelerators are network-attached, so the x86 server fleet is a first-class part of the end-to-end system: it runs critical-path runtime functions (for example orchestration, prompt caching, and IO/control services) and must be co-designed with software for token-level latency, throughput, and cost efficiency. You will translate workload behavior into CPU, memory, IO, PCIe, and host-networking requirements, drive platform evaluations with vendors, and provide technical leadership through qualification and production adoption in close partnership with other function leaders and TPMs.

## Responsibilities

- Own the architecture for all server roles in Cerebras clusters, including definitions of server types, configurations, and lifecycle strategy.
- Define and maintain server formulas (counts and ratios per CS-3 count, cluster size, and workload type) including capacity planning and headroom policy.
- Specify platform configurations: CPU SKU and core strategy, our vendor roadmap (e.g., AMD, Intel, ARM), memory topology (channels, DIMM type, capacity), PCIe topology and lane budgeting, NIC selection/placement, and local NVMe policy where applicable.
- Translate software and runtime flows into measurable hardware requirements (CPU utilization, memory bandwidth/latency, bursty IO patterns, queueing and concurrency limits) and communicate clear guardrails back to software teams.
- Develop performance and scaling models; validate with microbenchmarks and workload-level experiments; identify bottlenecks and drive cross-stack fixes.
- Define the OS, BIOS, firmware, and driver baseline for each server type; there are other teams that follow these recommendations and apply them on our fleet.
- Stay current on emerging server technologies (CPU generations, new memory technologies, CXL, NVMe evolutions, SmartNIC/DPU capabilities where relevant) and run proof-of-concept evaluations to determine when to adopt.
- Lead technical vendor engagements (OEM/ODM and component vendors): influence roadmap, request platform knobs, and drive joint debugging on performance or reliability issues.
- Define qualification and acceptance criteria (performance, stability, operability) and partner with the Infrastructure Hardware TPM to execute qualification plans and land changes cleanly into production.
- Support bring-up and rare deployment debugging in lab and staging environments; drive root-cause analysis for regressions spanning firmware, drivers, OS, and runtime behavior.

## Skills and Qualifications

- PhD. in Computer Science or Electrical/Computer Engineering and + 8 years industry experience, or Master’s/Bachelor’s in CS or EE + 10 years industry experience.
- 5+ years of experience in server platform architecture, systems performance engineering, or large-scale infrastructure design for AI/ML, HPC, or performance-sensitive distributed systems.
- Deep understanding of x86 server architecture: CPU microarchitecture basics, cache hierarchies, NUMA, memory controllers/channels, and memory bandwidth vs latency tradeoffs.
- Strong Linux systems knowledge: profiling and performance analysis, scheduling and syscall overheads, memory management behavior, and practical tuning methodology.
- Experience reasoning about high-performance IO paths, including NIC behavior at a systems level, RDMA/RoCE concepts, and NVMe performance characteristics.
- Proven ability to create capacity and performance models and validate them empirically with a rigorous benchmarking plan.
- Experience working directly with vendors/partners to evaluate platforms, drive issue resolution, and influence roadmaps.
- Strong cross-functional communication skills and ability to drive technical decisions through clear tradeoff documents and reviews.
- Familiarity with application and system software (C, C++, Python).

## 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:

- 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](https://www.cerebras.ai/join-us)! 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](https://www.cerebras.net/privacy/) to review our CCPA disclosure notice.

## About Cerebras Systems

## Company Overview
- **One-liner**: Cerebras builds wafer-scale AI chips (the largest semiconductors ever made) and supercomputers that deliver up to 15x faster inference than GPUs, offered both on-premises and via cloud APIs.
- **Entity Type**: Public (NASDAQ: CBRS) — filed for IPO and now listed on the Nasdaq.
- **Headquarters**: Sunnyvale, California, United States
- **Founded**: 2015
- **Founders**: Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie, Jean-Philippe Fricker

## Core Business
- **Primary industries**: Semiconductor manufacturing, AI computing hardware and software, cloud AI services.
- **Target customers**: B2B – enterprises, government research labs, cloud service providers, and AI developers.
- **Mission**: “Building impossible technologies so you can too” – accelerating AI through wafer-scale computing.

## Products & Services
- **Wafer-Scale Engine (WSE-3)**: The world’s largest AI chip (215 mm²), 58x larger than a typical GPU, built for ultra‑fast AI training and inference. Manufactured by TSMC.
- **CS-3 Supercomputer**: A single system powered by the WSE-3, delivering record-breaking AI performance for on-premises deployment.
- **Cerebras AI Inference Cloud & Training Cloud**: Pay-as-you‑go APIs that provide drop‑in OpenAI‑compatible access to Cerebras’ hardware for serving, fine‑tuning, and pre‑training models.
- **Condor Galaxy Network**: A series of supercomputers (e.g., CG‑1 with 4 exaFLOPs) built in partnership with G42 for high‑performance AI.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (recently public; market cap data not available in provided sources).
- **Key Metric**: Annual revenue of $78.7 M (latest); total funding of $2.7 B across 15 rounds.
- **Notable Investors/Partners**: G42, OpenAI (signed in 2026), Amazon Web Services (signed in 2026), Meta (Llama API), Perplexity, Mistral, Hugging Face, OpenRouter.
- **Growth Signals**: 
  - 34.3% YoY headcount growth (701 employees).
  - Rapid data center expansion across North America and Europe.
  - Won the HPCwire Readers’ & Editors’ Choice 2025 for Best AI Product/Technology.
  - Named to Forbes America’s Best Startup Employers 2026 and Fast Company Most Innovative Companies 2026 (AI).

## Competitive Advantages
- **Wafer‑scale architecture**: Eliminates interconnect bottlenecks, reducing latency and delivering up to 15x faster inference than GPU clusters.
- **Largest AI chip ever built**: 58x larger than any GPU, enabling massive on‑chip memory (SRAM) and compute density.
- **Full‑stack offering**: Hardware + cloud APIs + on‑prem systems, giving customers deployment flexibility.
- **Strong customer relationships**: Key contracts with OpenAI, AWS, G42, and Mayo Clinic (2024 Gordon Bell Prize work).

## Strategic Focus
- **Scale inference capacity**: Building out data centers to become the world’s #1 provider of high‑speed AI inference.
- **Deepen cloud partnerships**: Expanding pay‑as‑you‑go cloud access and enterprise deployments.
- **Advance wafer‑scale technology**: Continuous R&D on WSE‑3 successors and software stack (compiler, ML workflows).
- **Broaden model support**: Already serving Llama, Gemma, Qwen, Mistral, and more; integrating with Hugging Face and OpenRouter.

## Why Work Here
- **Culture**: “Extraordinary people, breakthrough innovation, global impact” – the company highlights collaborative, low‑overhead teams with little bureaucracy.
- **Work environment**: Hybrid/office with locations in Sunnyvale, San Diego, Toronto, and Bangalore. Inclusive and flexible policy.
- **Perks**: Premium medical/dental/vision, life insurance, generous vacation, 401(k) and Group RRSP retirement plans, daily catered meals, healthy snacks, family‑friendly events (including CEO’s famous BBQ).
- **Engineering focus**: Tackling fundamental challenges in chip design, system software, compiler technology, and ML workflows. Positions range from hardware (Design Verification, Manufacturing) to software (SRE, Compiler, Cloud).
- **Philanthrophy**: Supports local communities and hosts students from around the world for Q&A.

## Sources
1. [cerebras.ai/company](https://www.cerebras.ai/company)
2. [cerebras.ai](https://www.cerebras.ai/)
3. [cerebras.ai/join-us](https://www.cerebras.ai/join-us)
4. [linkedin.com/company/cerebras-systems](https://www.linkedin.com/company/cerebras-systems)

## Other roles at Cerebras Systems

- [Detection and Response Platform Engineer](https://feeny.ai/job/detection-and-response-platform-engineer-cerebras-systems-sunnyvale-gx4sa9aschnm) — Sunnyvale, CA
- [Physical Security Lead, Manufacturing Operations](https://feeny.ai/job/physical-security-lead-manufacturing-operations-cerebras-systems-sunnyvale-s741d6wava3v) — Sunnyvale, CA
- [Staff AI Engineer – Business Systems](https://feeny.ai/job/staff-ai-engineer-business-systems-cerebras-systems-sunnyvale-vgybt0ehv5wt) — Sunnyvale, CA
- [Senior ERP Systems Administrator](https://feeny.ai/job/senior-erp-systems-administrator-cerebras-systems-sunnyvale-z6p5ssmrtw01) — Sunnyvale, CA
- [DevOps Engineer Intern - PEY](https://feeny.ai/job/devops-engineer-intern-pey-cerebras-systems-toronto-8h2bj7zw2bd2) — Toronto, Canada
- [Senior Operations Accountant](https://feeny.ai/job/senior-operations-accountant-cerebras-systems-sunnyvale-1sfperr2mr6m) — Sunnyvale, CA
- [Software Engineer - Host and Network IO](https://feeny.ai/job/software-engineer-host-and-network-io-cerebras-systems-sunnyvale-tqykyeenvej3) — Sunnyvale, CA
- [Staff GPU Inference SDET](https://feeny.ai/job/staff-gpu-inference-sdet-cerebras-systems-sunnyvale-p8ke5xg4qba1) — Sunnyvale, CA
- [Network Security Engineer](https://feeny.ai/job/network-security-engineer-cerebras-systems-sunnyvale-s1q0rza0gbsf) — Sunnyvale, CA
- [Distributed Software Engineer](https://feeny.ai/job/distributed-software-engineer-cerebras-systems-toronto-2y0byqqxk57d) — Toronto, Canada