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Lambda

Cloud-based GPU services for AI training

Senior Software Development Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeBellevue, WA, USA

About the Job

Requirements

  • Have 6 or more years of professional software engineering experience building and operating production systems.
  • Write production-quality code and have deep expertise in multiple programming languages.
  • Have built automation or infrastructure tooling used by other engineers, such as provisioning systems, deployment pipelines, workflow engines, or internal APIs.
  • Have a solid grasp of distributed systems design, including API design, data modeling, idempotency, and handling partial failures.
  • Follow strong engineering practices, including code review, automated testing, continuous integration and continuous delivery, and git-based workflows.
  • Be comfortable using the Linux command line and understand core networking concepts such as TCP/IP, DNS, DHCP, VLANs, and IP routing.
  • Have led technical projects end to end, from design documentation through production rollout, and mentored other engineers.
  • Communicate clearly with software engineers, network engineers, and operations teams, and translate operational pain points into software requirements.

Responsibilities

  • Design, build, and operate software that automates Lambda's network lifecycle, from data center build-out and device provisioning through day-2 operations.
  • Build provisioning and zero-touch deployment systems that take network devices for new GPU clusters from racked to production-ready with minimal manual work.
  • Develop pipelines that generate, validate, and deploy network configuration as code, with automated testing, staged rollouts, and safe rollback.
  • Build capacity planning tools that combine inventory, topology, and utilization data to forecast port, bandwidth, optics, and IP address needs as Lambda's footprint grows.
  • Keep intended and live network state in sync by integrating automation with Lambda's source-of-truth, inventory, and IP address management systems.
  • Create application programming interfaces, services, and command-line interfaces that network engineers and other internal teams use to request, change, and audit network resources.
  • Instrument owned systems with metrics, logging, and alerting, and use that data to improve their reliability and speed.
  • Partner with Network Engineering, Data Center Operations, and platform teams to turn manual runbooks into reliable, self-service automation.
  • Participate in the on-call rotation for the automation systems and tooling the team owns.

Desired Qualifications

  • Experience in the networking domain, such as designing or operating data center networks using Clos or spine-leaf fabrics, BGP, EVPN/VXLAN, or ECMP.
  • Experience automating network devices from vendors such as Arista, Juniper, Cisco, or NVIDIA Cumulus/SONiC through their APIs, NETCONF/gNMI, or Jinja templating.
  • Familiarity with network automation tools and libraries such as Ansible, Nornir, Netmiko, NAPALM, or Terraform.
  • Experience building on network source-of-truth, data center infrastructure management, or IP address management systems.
  • Experience building capacity planning or forecasting tools, or working with large volumes of time-series telemetry.
  • Familiarity with high-performance computing or AI networking, such as InfiniBand or RoCEv2, at GPU cluster scale.
  • Experience running services on Kubernetes.
  • Understanding of data center power, space, and cooling constraints and how they shape build-out plans.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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