Network Architect

We are looking for a Network Architect to join our Cluster Engineering Team and help shape the front-end datacenter and interconnect fabric for the current and next generations of our AI clusters. You will partner closely with hardware vendors, internal networking teams, and industry peers to define best-in-class network architectures that deliver resilient, reliable, and high-throughput connectivity for large-scale AI workloads.

This is a deeply technical role that spans the full stack, from host-side networking and NIC behavior up through cluster-level coordination and requires fluency across diverse hardware components (including network switches, NICs, and the Accelerator Compute Engine) and the software layers that drive them. You will own proof-of-concept work for new network designs and features, and you will be the central technical voice for network reliability across the organization., * Design and architect front-end network fabrics for AI/ML and HPC clusters, optimizing for high resource utilization, low latency, and high-throughput communication.

• Build proof-of-concept implementations of new network designs and features, and drive them from prototype through production rollout.
• Identify and resolve performance and efficiency bottlenecks across the host-NIC-fabric
• Automate the deployment, configuration, and validation of network infrastructure using Python, including topology provisioning, fabric bring-up, config generation, and regression Strong programming skills are essential; this role builds tools, not just runbooks.
• Stand up and operate SRE-grade telemetry and observability for the cluster network: streaming telemetry (gNMI, OpenConfig, sFlow/IPFIX), metrics pipelines, alerting, and incident workflows. Define the SLIs/SLOs that govern network reliability and drive blameless post-incident analysis.
• Lead network debugging in large distributed-systems environments spanning multiple platforms and protocols, including deep dives into RoCEv2, PFC/DCQCN, ECMP hashing, congestion behavior, and packet-level forensics.
• Lead cross-functional, multi-phase technical projects spanning hardware, firmware, host networking, and cluster software.
• Collaborate with vendors and industry partners to shape network hardware and feature roadmaps.
• Represent the company in industry forums, standards bodies, and technical communities.
• Serve as the central point of contact for network reliability issues across the cluster.

Do you have experience in Managing projects in an engineering role?, Do you have a Master’s degree?, * D. in Computer Science or Electrical Engineering with 5+ years of industry experience, or Master’s in CS/EE with 10+ years of industry experience.

• 3+ years designing large-scale networks in datacenter and cloud environments.
• Extensive hands-on experience debugging networking issues in large distributed systems with multiple platforms and protocols.
• Demonstrated track record leading multi-phase, multi-team technical projects to completion.

Technical Skills

• Deep expertise across networking platforms: Juniper, Arista, Cisco, and open-box / disaggregated NOS architectures (SONiC).
• Strong working knowledge of networking protocols and fabric technologies: VXLAN, EVPN, RoCEv2, BGP, DCQCN, PFC, ECN, and streaming telemetry.
• Programming and automation: proficiency in Python (and/or Go) for building network automation, validation, and tooling. Comfort with config generation frameworks (Ansible, Jinja2), gNMI, and CI/CD pipelines for network infrastructure.
• SRE and observability: hands-on experience with streaming telemetry pipelines, time-series databases (Prometheus, InfluxDB), visualization (Grafana), log aggregation, and modern incident-management Ability to define SLIs/SLOs and instrument the network for proactive reliability.
• Familiarity with network visibility, management, and packet-capture/analysis tools., * Prior experience at hyperscalers or cloud service providers.
• Experience with AI/ML or HPC cluster networking, including lossless Ethernet design, rail-optimized topologies, and collective-communication traffic patterns.
• Track record of contributions to open-source networking projects, standards bodies, or industry conferences.

Cerebras Systems builds the world’s largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras’ current customers include top 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.

Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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., 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.