GPU-First Cloud Compute Engineer (Hybrid)

Lambda; San Francisco (CA); Hybrid; USD 266,000 - 395,000; Full time

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.