GPU-First Cloud Compute Engineer (Hybrid)

Lambda

San Francisco (CA)

Hybrid

USD 266,000 - 395,000

Full time

14 days+
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Benefits offered by this job

Health, dental, and vision coverage
Wellness stipend
Commuter stipend
401k with 2% match

Job summary

Lambda, The Superintelligence Cloud, is seeking a Software Engineer to build and optimize GPU-first cloud infrastructure supporting HPC and AI workloads. You will design, develop and maintain software for compute infrastructure, focusing on performance, scalability, and reliability, and help with bare-metal and VM instancing.

You will work across multiple teams to improve engineering workflows, contribute to platform tooling, and support high-velocity delivery.

Qualifications

  • 3+ years of experience with Go or Python in production environments.
  • 3+ years of experience with bare metal & virtualization hardware management and configuration.
  • Comfortable working in Linux environments and debugging at OS, hardware, and networking layers.
  • Ability to independently troubleshoot complex systems and communicate across teams.

Responsibilities

  • Design, develop and maintain software for GPU/CPU compute infrastructure with focus on performance, scalability, and reliability.
  • Implement and develop services for baremetal and VM instancing.
  • Develop distributed systems for managing compute resources across various SKUs.
  • Troubleshoot and debug complex issues in production and development environments.
  • On-call and incident ownership.
  • Collaborate across multiple teams and drive clarity in RFCs.

Skills

Go/Python
Bare metal
Linux
Troubleshooting

Tools

KVM
QEMU
Slurm
Kubernetes
Temporal

Job description

Lambda, The Superintelligence Cloud, is seeking a Software Engineer to build and optimize GPU-first cloud infrastructure supporting HPC and AI workloads. You will design, develop and maintain software for compute infrastructure, focusing on performance, scalability, and reliability, and help with bare-metal and VM instancing.

You will work across multiple teams to improve engineering workflows, contribute to platform tooling, and support high-velocity delivery.

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