Q

ML Engineer - AI Agent Design & Evaluation (Remote)

Remote
This Job is No Longer Active This position is no longer accepting applications

Design and evaluate autonomous AI agents across multiple LLMs for real-world domains. Provide expert human feedback to train Large Language Models for complex workflows. Requires backend engineering, AI automation, or systems integration experience with strong programming skills.

Key Highlights
Design and evaluate autonomous AI agents across multiple LLMs.
Provide expert human feedback for LLM training.
Requires experience in backend engineering, AI automation, or complex systems integration.
Key Responsibilities
Write evaluation rubrics with objective pass/fail criteria
Debug agent traces to identify failure patterns
Stress test agents against edge cases, prompt injection, and tool misuse
Assess production-grade modular software architecture
Analyse multi-turn system interactions and behaviours
Provide high-density technical feedback for LLM training
Technical Skills Required
Python JavaScript Go Java SQL
Benefits & Perks
Hourly compensation ranges from USD $30–$50
Weekly payments via PayPal or AirTM
Fully Remote
Nice to Have
Integrating agents with live tools such as Supabase, Gmail, and other APIs
Familiarity with persistent state and session-tracking patterns
Experience identifying privacy leaks, authority escalation, or indirect prompt injection vulnerabilities

Job Description

  • Job Title: ML Engineer (Remote)
  • Location: Remote (United States, Canada, United Kingdom, Australia)
  • Work Mode: Fully Remote

Role Overview

Help design and evaluate autonomous AI agents across multiple LLMs, spanning health, education, daily life, and other real-world domains (all coding work). Shape the future of agentic AI systems by providing expert human feedback to leading AI organisations. Help train Large Language Models (LLMs) for complex, multi-step architectural workflows.

Key Responsibilities

AI Agent Evaluation

  • Write evaluation rubrics with objective pass/fail criteria
  • Debug agent traces to identify failure patterns
  • Stress test agents against edge cases, prompt injection, and tool misuse
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