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Clera

Platform Engineer

San Francisco$150k–$250kfulltimemidAdded today

About this role

A backend-focused platform engineering role at an AI/ML infrastructure startup, responsible for owning production reliability, scale, performance, and developer experience across core cloud services. You'll design and operate AWS infrastructure, build CI/CD systems, and ensure the platform runs efficiently and safely at scale.

What you'll do

  • Own production uptime, latency, deployment speed, and incident response for core platform services
  • Build and maintain AWS infrastructure using Terraform, Kubernetes/EKS, Docker, EC2, and related services
  • Design backend systems for scale including capacity planning, autoscaling, queueing, and failure recovery
  • Define and improve observability through dashboards, alerts, logs, SLOs, runbooks, and on-call processes
  • Build reliable CI/CD pipelines and release automation to reduce production risk and improve developer velocity
  • Write production code to automate infrastructure tasks and create internal developer tooling

What they're looking for

  • AWS (EC2, EKS, S3, IAM, networking, secrets management)
  • Terraform and infrastructure-as-code
  • Kubernetes and containerization (Docker, Helm)
  • Backend service architecture and system design
  • CI/CD pipeline design and automation
  • Observability, monitoring, and incident response
  • Production-quality backend coding
  • Database and async systems design

Benefits

  • Salary: $150,000–$250,000 USD annually
  • Significant ownership and direct impact at early-stage, well-funded startup
  • Work with world-class technical team on AI infrastructure
  • Visa sponsorship available
  • On-site in San Francisco or Singapore, with fully remote contractor option
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a time you owned production uptime and latency for a user-facing service—what metrics did you track and how did you respond to incidents?
  • Describe your experience designing and operating Kubernetes/EKS clusters in production—what scaling or reliability challenges have you solved?
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