Network Engineer
Gimlet · San Francisco
- On site
- Fulltime
- San Francisco
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Subject: Interested in the Network Engineer role at Gimlet
Hi Taylor — I came across the Network Engineer opening and wanted to reach out directly. I've spent the last few years doing exactly this kind of work, and Gimlet stood out because…
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About the role
About Us
Gimlet is building the next generation of AI infrastructure: large-scale AI datacenters and the orchestration platform that coordinates them.
The future of AI will require vastly more compute than exists today. But as AI workloads become more complex and new hardware architectures emerge, simply deploying more GPUs isn't enough. The challenge is making increasingly diverse compute work together.
Gimlet's platform intelligently partitions and routes workloads across heterogeneous hardware, enabling step-function improvements in performance and efficiency. Customers deploy through production-grade APIs without needing to think about hardware selection, placement, or optimization.
We work with foundation labs, hyperscalers, and AI-native companies to power production workloads at massive scale and help define the infrastructure layer for the future of AI.
About this Role
Gimlet Labs is seeking a Network Engineer to design, build, and scale the network infrastructure powering production-scale AI and distributed systems for frontier labs, hyperscalers, and other high-performance compute environments.
This is an opportunity to build the network foundation for systems serving real production traffic at massive scale, while also shaping the network architecture for the next generation of AI datacenters. You will help determine how future high-performance compute environments are designed, deployed, interconnected, and operated.
The ideal candidate has deep technical knowledge of modern data center networking and is comfortable operating across physical infrastructure, network architecture, deployment workflows, and operational troubleshooting. We are looking for someone who can operate independently, drive infrastructure improvements, and help build scalable networking foundations for high-performance compute and AI workloads.
What you will work on
Design, deploy, and scale datacenter network infrastructure supporting AI workloads, distributed systems, and high-performance compute environments.
Lead network provisioning, device configuration, connectivity validation, deployment testing, and production turn-up activities for new infrastructure builds and hardware expansions.
Build and maintain scalable network topology designs, IPAM, deployment standards, operational documentation, and infrastructure readiness processes.
Troubleshoot complex networking, routing, hardware, connectivity, and performance issues across physical infrastructure and distributed systems environments.
Partner closely with infrastructure, systems, deployment, and operations teams to improve network reliability, deployment velocity, operational readiness, and infrastructure scalability.
Drive automation and operational improvements across provisioning, configuration management, monitoring, deployment validation, and incident response workflows.
You may be a good fit if
Have experience designing, deploying, and operating production network infrastructure.
Have strong networking fundamentals across routing, switching, connectivity, performance, and reliability.
Have worked with spine-leaf or Clos fabrics, backbone or WAN networks, ECMP, BGP, EVPN, VXLAN, and routing policies.
Understand high-performance AI/HPC networking concepts such as RoCEv2, InfiniBand, lossless Ethernet, QoS, DSCP, queuing, shaping, LAGs, optical transport, DWDM, coherent optics, and traffic engineering.
Can troubleshoot complex issues across hardware, software, network, and distributed systems boundaries.
Enjoy building systems, automating workflows, and improving operational processes.
Work well across engineering, infrastructure, deployment, and operations teams.
Take ownership end-to-end and operate effectively in ambiguous, fast-moving environments.
Strong candidates may also have
Experience with AI/HPC, GPU, or large-scale distributed infrastructure.
Knowledge of AI application traffic patterns, including collective operations and workload colocation strategies that optimize network performance.
Experience with cloud networking on GCP, AWS, Azure, or similar platforms.
Experience with Arista, Cisco, Juniper, or NVIDIA networking platforms, as well as Palo Alto Networks PAN-OS.
Experience with network automation using Python, Ansible, Terraform, or similar tooling.
Familiarity with RDMA, RoCE, InfiniBand, or other high-performance networking environments.