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Senior Software Engineer (Fleet Engineering)

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Onsite

Senior Software Engineer (Fleet Engineering)

Lambda

Senior Software Engineer (Fleet Engineering)

Skills

PythonGolangMachine LearningAIAnsibleLinuxKubernetes

2 months ago

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Important information


Contract type:

Permanent

Salary:

$225,000 – $395,000 / year

Location:

United States

Work mode:

Onsite

Published on:

12 August 2026

What they need


  • Fleet Engineering at Lambda is responsible for logically deploying, provisioning, and maintaining our rapidly growing fleet, spanning GPU, CPU, and storage hosts along with the InfiniBand and networking fabric that ties them together
  • As a Senior Software Engineer in Fleet Engineering, you will build and improve the systems that take our clusters from hardware receiving through provisioning, configuration, and day-to-day operation in production
  • You will work on automation and tooling that keep the fleet consistent, healthy, and easy to operate as it grows. The systems you build directly determine how quickly and reliably new hardware turns into usable customer capacity
  • Fleet Engineering spans several teams. Depending on the team you join, your day-to-day will involve some combination of the following:
  • Develop and Maintain Production Systems: Design, implement, and improve the software that powers fleet lifecycle management, machine configuration, and cluster state at scale
  • Automate Provisioning and Deployment: Build and enhance automation that takes clusters from logical design and racking through OS provisioning, configuration, validation, and customer hand-off
  • Support New Hardware and Site Bring-Up: Enable bring-up, validation, and production readiness for new server, accelerator, and network platforms, as well as new datacenter sites
  • Improve Machine Lifecycle Workflows: Refine bare metal provisioning, firmware and DPU updates, imaging, and system health monitoring across the fleet
  • Keep Fleet State Consistent and Healthy: Build systems that reconcile intended against actual configuration, catch drift before it causes deployment failures, and maintain production SLAs
  • Debug Hardware and Firmware Issues: Investigate failures across BIOS, BMC, firmware, DPUs, networking, storage, and boot flows
  • Collaborate Across Teams: Work closely with datacenter and deployment operations, networking, architecture, security, and product engineering teams to build scalable, maintainable solutions

Benefits

  • Health, dental vision
  • 401k match
  • 5 sick days and 12 paid holidays
  • Flexible PTO
  • Paid parental, medical, and caregiver leave- Are fluent in Python, Go, or similar, and comfortable with APIs, distributed systems, and automation pipelines
  • Work confidently in Linux environments and can debug across the OS, hardware, and networking layers
  • Leave systems, and the teammates around you, better than you found them
  • Have owned production systems with real SLAs
  • Can lead technical design on medium-to-large features: take an ambiguous problem, write the doc, drive alignment across teams, and ship
  • Are excited about working at the intersection of hardware, software, and physical datacenter builds
  • Have 5+ years of engineering experience
  • Experience in AI or ML infrastructure, or other hyperscale compute environments
  • Hands-on experience with bare metal provisioning and lifecycle management, including technologies such as PXE, Redfish, IPMI, BMC, DHCP, and DNS
  • Experience diagnosing issues involving drivers, firmware, and hardware compatibility across GPU servers
  • Familiarity with datacenter physical infrastructure, including racks, switches, InfiniBand fabric, and power domains
  • Experience with DPUs or programmable network accelerators
  • Experience building Linux distributions or managing OS customization and imaging
  • Experience with network source-of-truth systems (NetBox or similar) or DCIM tooling
  • Familiarity with Ansible or similar configuration management tooling
  • Exposure to Kubernetes and container orchestration concepts
  • Experience incorporating AI-assisted development tools into engineering workflows, including code generation, debugging, test development, and documentation

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