Remote LLM Agent Engineer — Freelance

Outlier; Portland (OR); Remote; USD 57,000 - 81,000; Part time

Benefits offered by this job

Weekly payments

Remote work

Free AI access (>$200/mo)

Job summary

Outlier is seeking talented contributors to shape the future of autonomous agents like OpenClaw. This freelance, remote role offers pay up to 50 USD per hour and weekly payments, with opportunities to earn additional incentives through Missions and AI access perks.

Join a collaboration that trains LLMs to design, coordinate, and optimize complex workflows. We welcome backend engineers, AI automation experts, and developers who thrive on live-system challenges and high-density feedback loops.

Qualifications

• 2+ years of experience in backend engineering, AI automation, or complex systems integration.
• Ability to build and maintain production-grade software with modular separation (data parsing, logic processing, reporting).
• Experience in live, non-mocked environments and multi-turn system interactions.
• Attention to detail and the ability to provide high-density technical feedback on complex system behaviors.
• Nice to have experience building multi-stage coordination tasks and integrating agents with live tools.

Responsibilities

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

Skills

Backend engineering

AI automation

Systems integration

Modular software design

Live environments

Detail oriented

Technical feedback

Coordination tasks

API integrations

Memory/state management

Privacy awareness

Tools

Supabase

Gmail

APIs

Job description

Outlier is seeking talented contributors to shape the future of autonomous agents like OpenClaw. This freelance, remote role offers pay up to 50 USD per hour and weekly payments, with opportunities to earn additional incentives through Missions and AI access perks.

Join a collaboration that trains LLMs to design, coordinate, and optimize complex workflows. We welcome backend engineers, AI automation experts, and developers who thrive on live-system challenges and high-density feedback loops.