Platform Engineer — AI-Powered Internal Tooling

Span

United States

On-site

USD 140,000 - 200,000

Full time

14 days+

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Job summary

Span is seeking an engineer to build and scale the internal platform and tooling that helps engineering teams move faster and more reliably. You will work on an AI-native developer tooling stack, create a developer portal, and drive automation across operations and development workflows.

The role focuses on improving developer productivity through thoughtful tooling, automation, and LLM-driven solutions that reduce cognitive load and operational overhead.

Qualifications

  • Experience building platform engineering and internal tooling.
  • Early engineering hire, founder, or meaningful solo projects.
  • Strong systems thinking to improve engineering productivity.
  • Experience building with LLMs, AI agents, and agentic workflows.

Responsibilities

  • Build AI-powered platform tooling that identifies root causes of recurring work and automates tasks.
  • Create an internal developer platform with a software catalog, service ownership model, and engineering portal.
  • Improve developer experience with workflows, automation, and self-service infra.
  • Leverage LLMs and AI agents to automate platform operations, incident response, and docs.
  • Collaborate with engineering teams to identify platform gaps and improve reliability.

Skills

Platform engineering
Developer infrastructure
DevEx
SRE
Internal tooling
LLMs / AI agents
System architecture

Tools

TypeScript
NestJS
React
Python
Postgres
Redis
Kafka
Airbyte
Dagster
AWS
Kubernetes
FluxCD

Job description

Span is seeking an engineer to build and scale the internal platform and tooling that helps engineering teams move faster and more reliably. You will work on an AI-native developer tooling stack, create a developer portal, and drive automation across operations and development workflows.

The role focuses on improving developer productivity through thoughtful tooling, automation, and LLM-driven solutions that reduce cognitive load and operational overhead.

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