📋 Description

• Own the platform: GCP, Kubernetes, Temporal, GPU fleet, deploy and rollback machinery
• Build AI enablement substrate: GPU capacity, training/inference pipelines, serving costs
• Treat cost as an engineering constraint
• Ensure security: identity, access, secrets, least-privilege, supply chain
• Make systems legible: infrastructure-as-code, runbooks, observability
• Improve team learning and shipping practices

🎯 Requirements

• 8+ years building and operating production distributed systems
• Leverage agents to multiply impact and think critically about AI use
• Experience running systems where failure was expensive
• Carried a pager, commanded incidents, and rolled back
• Use SLOs and error budgets as operating tools
• Experience with a major cloud provider and Kubernetes in production

🎁 Benefits

• Generous healthcare package
• 401k matching program
• Catered lunches
• Flexible vacation time
• Equity