About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Network Engineer, you will build the network infrastructure connecting Gimlet’s compute, storage, and distributed systems. Your work will shape how our data centers are designed, deployed, and operated as the platform scales.
You will work across network architecture, physical infrastructure, provisioning, deployment, and production operations. You will design high-performance network fabrics, bring new sites and hardware online, automate operational workflows, and troubleshoot performance and reliability issues across network and systems boundaries.
What success looks like
In the first 12-18 months, you will:
Design and deploy network infrastructure for new data centers and hardware expansions
Improve network performance and reliability for distributed AI workloads
Automate provisioning, configuration, validation, and monitoring workflows
Establish repeatable network standards that make infrastructure deployments faster and more reliable
Troubleshoot complex issues across network, hardware, and distributed systems boundaries
You may be a good fit if you have
Experience designing, deploying, and operating production data center networks
Strong fundamentals in routing, switching, performance, and reliability
Experience with BGP, ECMP, EVPN, VXLAN, and spine-leaf or Clos fabrics
Knowledge of high-performance networking technologies such as RDMA, RoCE, InfiniBand, or lossless Ethernet
The ability to troubleshoot across network, hardware, software, and distributed systems boundaries
Experience automating network infrastructure with Python, Ansible, Terraform, or similar tools
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.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
Skills Required
- Experience designing, deploying, and operating production data center networks
- Strong fundamentals in routing, switching, network performance, and reliability
- Experience with BGP, ECMP, EVPN, VXLAN, and spine-leaf or Clos network fabrics
- Knowledge of RDMA, RoCE, InfiniBand, or lossless Ethernet
- Ability to troubleshoot across network, hardware, software, and distributed systems boundaries
- Experience automating network infrastructure with Python, Ansible, Terraform, or similar tools
- Experience with AI/HPC, GPU, or large-scale distributed infrastructure
- Knowledge of AI application traffic patterns, collective operations, and workload colocation strategies
- Experience with cloud networking on GCP, AWS, Azure, or similar platforms
- Experience with Arista, Cisco, Juniper, or NVIDIA networking platforms and Palo Alto Networks PAN-OS
What We Do
Gimlet Labs builds an AI inference cloud and software stack that routes and orchestrates agent workloads across heterogeneous processors, including GPUs, CPUs, and other architectures. Its technology can divide a model's execution among different chips, improving inference speed, throughput, and efficiency. The company combines applied AI research with managed multi-silicon data centers and software customers can deploy in their own facilities, aiming to expand compute available for AI.
