FPGA Engineer

Posted Yesterday
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2 Locations
Hybrid
Senior level
Artificial Intelligence • Hardware • Software • Semiconductor
The Role
Design and deliver production FPGA solutions for chassis-to-wafer IO including RoCE v2/RDMA network interfaces, switching fabric, and serial IO. Develop RTL, produce bitstreams, optimize bandwidth and latency, debug large AI cluster networking, coordinate board bringup, and lead cross-functional projects with DV, embedded software, and architecture teams.
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 Host and Network IO Team develops the full IO path implementation between a distributed system of server nodes, through the cluster, down to the custom RoCE network stack implemented in Cerebras' system, and over the proprietary IOs onto the WSE. As an FPGA developer on the team, you will own the in-chassis IO subsystem consisting of i) several cluster-facing RoCE v2 network interfaces via a custom implementation of the RDMA protocol; ii) a large programmable switching fabric; and iii) Serial IO communication with the Cerebras WSE via a proprietary protocol. You will interface between AI application-level IO teams, cluster architecture teams, and embedded software teams to develop solutions that optimize bandwidth and latency while minimizing congestion, pauses, pause spreading, unfairness, etc. The scope of work spans multiple generations of hardware products from improvements to presently deployed hardware, implementation of upcoming systems, and design/architecting of future next-gen architectures.

Responsibilities
  • Lead full chassis-to-wafer IO architecture and design

  • Improve current RTL, implement next-gen FPGA design, define future IO architectures

  • Produce production-ready bitstreams for deployment into large clusters running customer inference services

  • Work with DV team to prevent bug slips and simplify debug

  • Optimize bandwidth/latency over FPGA datapath and all IO interfaces

  • Interface with board team facilitating and executing board bringup

  • Drive network performance debug of large AI clusters

  • Integrate leading edge networking technologies and protocols

  • Lead cross-functional technical projects spanning multiple teams and integrating diverse software and hardware components to deliver an improved network IO solution.

  • Foster clear and effective communication across teams and stakeholders.

Skills & Qualifications
  • 5+ years industry experience generating production FPGA solutions, OR Master's/PhD in Computer or Electrical Engineering + 3 years industry experience,

  • Proficiency in Verilog development in Git based repo

  • Highly productive with FPGA placement & routing, timing closure, simulation, debug, and other FPGA tools/workflows

  • Network protocol familiarity (TCP, RoCE) and network debug tools such as Wireshark

  • Knowledge of network switch environments or willingness to learn (Arista, Juniper, etc.).

  • AI-augmented development environment

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

  • 5+ years industry experience generating production FPGA solutions OR Master's/PhD in Computer or Electrical Engineering + 3 years industry experience
  • Proficiency in Verilog development in a Git-based repository
  • Experience with FPGA placement & routing, timing closure, simulation, and debug workflows/tools
  • Familiarity with network protocols (RoCE v2, RDMA, TCP) and network debug tools such as Wireshark
  • Experience producing production-ready FPGA bitstreams for deployment in large clusters
  • Knowledge of network switch environments (Arista, Juniper) or demonstrated willingness to learn
  • Experience with serial IO communication and high-performance IO subsystem design
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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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