Remote Backend Engineer for AI Agents

Outlier; Australia; Hybrid; AUD 59,000 - 98,000; Full time

Benefits offered by this job

Remote work

Flexible schedule

Weekly payments

Job summary

Outlier helps the world’s most innovative companies improve their AI agents by providing human feedback. Do you want to shape the future of autonomous agents like OpenClaw?

We collaborate with leading AI organizations to train Large Language Models (LLMs) to function as proactive, multi-step agents. Our projects focus on teaching these systems how to design, coordinate, and optimize complex, real-world architectural workflows.

Qualifications

• 2+ years of experience in backend engineering, AI automation, or complex systems integration.
• Proven ability to build and maintain production-grade software with modular separation (e.g., distinct services for data parsing, logic processing, and reporting).
• Practical experience building for live, non-mocked environments and handling multi-turn system interactions.
• Outstanding attention to detail and the ability to provide clear, high-density technical feedback on complex system behaviors.
• Nice to have: expertise with multi-stage coordination tasks and live tool integrations (e.g., Supabase, Gmail, APIs).
• Knowledge of persistent state tracking and session discovery.
• Experience identifying privacy leaks, authority escalation, or indirect prompt injections.

Responsibilities

• Write Eval Rubrics
• Debug Agent Traces
• Stress Test Agent

Skills

Backend engineering

AI automation

Complex systems integration

Modular architecture

Live environments/multi-turn systems

Attention to detail

Technical feedback

Coordination tasks

API integration

Persistent state tracking

Privacy/security issues

Tools

Supabase

Gmail

APIs

Job description

Outlier helps the world’s most innovative companies improve their AI agents by providing human feedback. Do you want to shape the future of autonomous agents like OpenClaw?

We collaborate with leading AI organizations to train Large Language Models (LLMs) to function as proactive, multi-step agents. Our projects focus on teaching these systems how to design, coordinate, and optimize complex, real-world architectural workflows.