Console
Platform Engineer
About this role
Console seeks a Platform Engineer to design and own the infrastructure layer supporting autonomous AI agents for enterprise operations. You'll architect systems for scale, build cloud infrastructure, and create deployment pipelines while working directly with the CTO at this well-funded AI startup.
What you'll do
- Architect and deploy a self-hostable version of Console with enterprise security and data isolation
- Design and evolve production deployment systems that remain fast, reliable, and downtime-free at scale
- Build and manage cloud infrastructure across AWS/GCP with focus on performance, cost, and reliability tradeoffs
- Improve developer experience through better tooling, workflows, and build/deployment systems
- Implement safer release practices like blue-green deployments to enable rapid shipping without breaking changes
What they're looking for
- Backend or platform engineering (5+ years)
- AWS and/or GCP cloud infrastructure
- Infrastructure as Code (Terraform or Pulumi)
- TypeScript/Node/React stack
- Production-grade system design and scaling
- Developer experience optimization
- Deployment and CI/CD systems
- High-scale distributed systems
Benefits
- Competitive compensation with early exercise equity and QSBS eligibility
- Comprehensive health, dental, and vision insurance
- Unlimited PTO
- 401(k) plan
- Daily meals provided in office
- Impact-focused growth opportunities from early-stage company
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Console
Console builds an AI agent platform that automates enterprise operations across IT, HR, Finance, and other functions by turning AI capabilities into actionable workflows. The company is hiring Infrastructure Engineers, Platform Engineers, Product Engineers, Applied AI Engineers, and Forward Deployed Engineers to develop scalable cloud infrastructure, optimize intelligent agent systems, and lead enterprise customer implementations.
View all jobs at ConsoleLikely interview questions
- Walk us through a time you built or significantly improved a CI/CD system. What was the biggest bottleneck you removed, and how did you measure impact?
- How do you approach designing verification environments that need to validate behavior across multiple integrations and agent workflows without becoming brittle or slow?