LiveFlow

LiveFlow

Graduate Engineer - AI Agents

San Francisco · Exec

Sponsorship not specifiedDetected 77 days ago
TypeScriptElixirNext.jsFull-Stack DevelopmentAWSTerraformLLMsAgentic AIA/B TestingAccounting

About the role

  • Learning how finance teams operate and identifying opportunities to improve their workflows
  • Contributing to AI agent workflows that simplify and automate complex tasks
  • Improving internal tooling and engineering workflows

Responsibilities

  • You should be excited by early ownership and building things that matter.
  • You'll work closely with experienced engineers as well as product and design, and quickly build both technical depth and product intuition.
  • We value engineers who think about how to build faster and more effectively
  • We're hiring Graduate Engineers to help build AI agents that power the next generation of LiveFlow.

Requirements

  • Recent graduate or early-career engineer (0-2 years experience)
  • Ability to operate in a fast-paced environment with ambiguity
  • Experience with any backend or frontend frameworks
  • You don't need prior industry experience - but you do need strong fundamentals, high curiosity, and the ability to learn quickly.

Skills

  • AWS, Terraform
  • You'll have access to the latest AI coding tools (e.g. Cursor, Claude)
  • Agents can take actions across the platform via tools, APIs, and workflows

Compensation

  • Competitive salary + equity

Benefits

  • Competitive salary + equity
  • Health insurance
  • Learning budget (books, courses, conferences)
  • Unlimited vacation

Company info

  • LiveFlow is building the next-generation accounting and finance platform - enabling lean finance teams to run massive enterprises.
  • At LiveFlow, you'll contribute to real product features from day one and see your work shipped to customers quickly.
  • We're looking for engineers who are curious, ambitious and want to accelerate their growth in a fast-moving environment.

This listing is sourced directly from LiveFlow's careers page and normalized into a canonical job model.