网络工程师，工程研发环境

Own end-to-end frontend and backend network design, deployment, and operations for AI and compute lab clusters

Serve as a primary networking point of contact for backend fabrics, including Arista and internally developed network OS-based scale-out networks supporting AI workloads

Design, deploy, and support high-throughput, low-latency cluster networking, including congestion management (PFC/ECN), RDMA validation, and lossless transport

Perform hands-on troubleshooting and root-cause analysis across L1–L4 using packet captures, telemetry, and vendor tools to resolve complex lab issues

Support silicon, hardware, and software bring-ups, ensuring reliable connectivity and on-time validation

Lead and execute lab network lifecycle activities, including upgrades, migrations, capacity expansions, and decommissioning across regions

Develop and maintain network automation, configuration templates, and zero-touch provisioning (ZTP) workflows

Create and maintain MOPs, runbooks, and readiness checklists for internal teams and vendor executions

Provide direct consultation and training to cross-functional partners, enabling teams to operate and troubleshoot lab networks

Collaborate closely with hardware, software, systems, and lab operations teams to validate new platforms, optics, and network designs

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience

10+ years of experience designing, deploying, and operating network infrastructure in production or lab environments

Experience working in multi-vendor network environments (e.g., Arista, Cisco, Juniper, open network OS-based platforms)

Experience with configuration management, code repositories, and zero-touch provisioning (ZTP) for network infrastructure

Experience with IPv4/IPv6, L2/L3 protocols, including STP, OSPF, BGP, TCP/IP, DHCP, DNS, VLANs, VRRP, LACP, MC-LAG, ACLs, MACsec, and EVPN/VXLAN

Working knowledge of scripting or programming languages (e.g., Python, shell) for automation and tooling

Demonstrated experience to operate consistently while working under your own initiative, seeking feedback and input where appropriate in a global, time-critical environment, managing multiple priorities and mission-critical timelines

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy review)

Hands-on experience with disaggregated networking products and software, such as Meta's open network OS (FBOSS), SONiC, Cumulus Linux, or equivalent open networking platforms

Experience with network automation, CI/CD pipelines, audit frameworks, and validation tooling

Networking L1 expertise in validating multi-vendor optics, with proficiency in using the BCM shell and I2C utilities to troubleshoot hardware-level issues

Hands-on experience with lab test equipment, optics qualification (e.g., 400G/800G), optical switches and physical infrastructure

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Understanding of physical infrastructure design, including structured cabling, space, power, and cooling systems

Experience supporting AI/ML or high-performance compute clusters in lab or pre-production environments

Hands-on experience with backend cluster networking, including scale-out fabrics, RDMA networks, and congestion management

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Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.

10+ years of experience designing, deploying, and operating network infrastructure in production or lab environments.

Experience working in multi-vendor network environments (e.