AI Cloud Deployment Success Engineer

Socket.dev; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Health, dental, vision coverage

401(k) with 2% company match

Wellness and commuter stipends

Flexible PTO

Job summary

Lambda is seeking a Superintelligence Technical Success Engineer in San Francisco/San Jose to drive deployments from contract to live production. You will validate configurations, troubleshoot complex issues, and coordinate with Infrastructure, Engineering, Product and Data Center teams to deliver reliable production workloads.

Ideal candidates have 4+ years in GPU/HPC, cloud, Kubernetes, or large-scale Linux, with strong communication and a bias toward ownership.

Qualifications

• 4+ years hands-on technical experience in GPU/HPC infrastructure, cloud platforms, Kubernetes, or large Linux systems.
• Strong troubleshooting and problem-solving abilities in complex environments.
• Excellent written and verbal communication for status updates and docs.
• Ability to coordinate across engineering and infrastructure teams.

Responsibilities

• Take signed deployments from contract to live production environments.
• Validate configuration, connectivity, storage, and compute against promises.
• Drive issues to resolution with engineering and infrastructure teams.
• Coordinate with multiple teams to close blockers and dependencies.
• Maintain an accurate deployment technical picture and risks.
• Provide clear status updates to stakeholders (RAG, risks, actions).
• Guide customers through onboarding to first production workload.
• Be the primary technical contact during deployment and early production.

Skills

GPU/HPC infra

Kubernetes

Cloud platforms

Linux systems

Job description

Lambda is seeking a Superintelligence Technical Success Engineer in San Francisco/San Jose to drive deployments from contract to live production. You will validate configurations, troubleshoot complex issues, and coordinate with Infrastructure, Engineering, Product and Data Center teams to deliver reliable production workloads.

Ideal candidates have 4+ years in GPU/HPC, cloud, Kubernetes, or large-scale Linux, with strong communication and a bias toward ownership.