Backend Engineer (AI)

TL;DR

Backend Engineer (AI): Design, build, and operate scalable backend services and platform infrastructure for user-facing applications, with an accent on cloud-native reliability, scalability, and performance. Focus on Kubernetes-based operations, observability and deployment workflows, and building AI-assisted internal tooling/coding agent capabilities that translate specs into production-ready features.

Location: US-CA-Menlo Park (Hybrid)

Salary: $200K–$287.5K

Company

hirify.global builds an AI Data Cloud platform powering user-facing applications and developer experiences.

What you will do

• Design, build, and operate scalable backend services and platform infrastructure for user-facing applications.
• Own features end-to-end: technical design, implementation, testing, deployment, and operational excellence.
• Improve system reliability, scalability, performance, and developer productivity.
• Build and operate cloud-native infrastructure and services running on Kubernetes; enhance deployment, observability, and operational workflows.
• Debug and resolve complex production issues across distributed systems and Kubernetes infrastructure.
• Build internal tools, including an AI coding agent/orchestrator, to enable self-serve end-to-end production feature creation via prompts.

Requirements

• 6+ years of experience designing and building large-scale distributed or user-facing systems in production.
• Strong backend engineering fundamentals with hands-on experience building reliable, scalable backend systems.
• Production experience building and operating services on Kubernetes.
• Strong understanding of cloud-native architectures, container orchestration, service reliability, and observability.
• Experience with distributed systems, APIs, cloud infrastructure, or developer platforms.
• Strong coding and debugging skills; ability to navigate complex system architectures.

Culture & Benefits

• Hybrid work model based in Menlo Park, California.
• Emphasis on reliability, scalability, maintainability, and performance in engineering decisions.
• Operational ownership with focus on improving deployment, observability, and incident resolution.
• Collaborative environment working with engineers and product managers across cross-functional teams.
• Mentoring, knowledge sharing, and code reviews as part of day-to-day engineering practice.

Hiring process

• Interviews focused on backend engineering experience, distributed systems, and Kubernetes operations.
• Technical evaluation of design/implementation and debugging approach for production issues.
• Discussion of collaboration style and engineering ownership practices.