AI Rubric Designer (Remote)
Quik Hire Staffing · France
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- Hybrid
- Part-time
- $50,000 / year
- France
Job highlights
- Design rubrics to evaluate AI agent outcomes.
- Test and debug AI agents in real-world scenarios.
- Provide technical feedback for LLM training.
- Requires 3+ years backend engineering experience.
- Flexible remote work with weekly payments.
About the role
AI Rubric Designer (Remote)
Quik Hire Staffing is seeking an AI Rubric Designer for a fully remote position. This role involves building agents using OpenClaw across multiple AI models and designing rubrics to evaluate their outcomes in areas such as health, education, and daily life. You will shape the future of autonomous AI agents by providing expert human feedback to leading AI organizations and training Large Language Models (LLMs) for complex, multi-step architectural workflows. This is a flexible remote role with no minimum hours and weekly payments.
Key Responsibilities
- AI Agent Testing: Write evaluation rubrics with objective pass/fail criteria.
- Debugging: Debug agent traces to identify failure patterns.
- Stress Testing: Stress test agents in multi-step, real-world scenarios.
- Technical Evaluation: Assess production-grade modular software architecture.
- Analysis: Analyze multi-turn system interactions and behaviours.
- Feedback: Provide high-density technical feedback for LLM training.
Project Workflow
- Create an account and upload a resume/ID.
- Complete an onboarding assessment.
- Start earning through flexible task assignments.
Qualifications
- 3+ 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).
- Strong command of at least two major languages (e.g., Python, JavaScript, Go, or Java) and experience working with SQL databases.
- Practical experience building for live, non-mocked environments and handling multi-turn system interactions.
Compensation
Hourly compensation ranges from USD $30–$50, depending on experience and task complexity. Payments are issued weekly via supported payout platforms (e.g., PayPal or AirTM). Full compensation details are provided prior to task acceptance.
Equal Opportunity Statement
Selection decisions are based solely on skills, qualifications, and project requirements. We are committed to inclusive and fair engagement practices and consider all qualified applicants without regard to legally protected characteristics.
Key skills/competency
- AI Rubric Design
- AI Agent Testing
- LLM Training
- Backend Engineering
- AI Automation
- Complex Systems Integration
- Software Architecture
- Technical Feedback
- Python
- JavaScript
Skills & topics
- AI Rubric Designer
- AI Agent Testing
- LLM Training
- Backend Engineering
- AI Automation
- Complex Systems Integration
- Software Architecture
- Technical Feedback
- Remote
- Finland
- France
- Italy
- Norway
- Python
- JavaScript
- Go
- Java
- SQL
How to get hired
- Tailor your resume: Highlight backend engineering, AI automation, and complex systems integration experience, emphasizing modular software architecture and multi-turn interaction handling.
- Showcase technical skills: Clearly list your proficiency in languages like Python, JavaScript, Go, or Java, and your experience with SQL databases and live environments.
- Prepare for assessments: Be ready to demonstrate your ability to write objective evaluation rubrics and debug complex AI agent traces.
- Highlight relevant experience: Emphasize any experience in AI agent testing or providing technical feedback for LLM training to align with the role's core responsibilities.
Technical preparation
Master Python, JavaScript, Go, or Java.,Practice SQL database interactions.,Build modular software in live environments.,Understand AI agent trace debugging.
Behavioral questions
Describe a complex system you integrated.,How do you define objective evaluation criteria?,Share an instance of debugging AI failures.,How do you provide constructive technical feedback?
Frequently asked questions
- What are the primary responsibilities of an AI Rubric Designer at Quik Hire Staffing?
- As an AI Rubric Designer at Quik Hire Staffing, your primary responsibilities include writing evaluation rubrics with clear pass/fail criteria, debugging AI agent traces to find failures, stress-testing agents in complex scenarios, assessing software architecture, and providing high-density technical feedback for LLM training.
- What qualifications are needed for the AI Rubric Designer role?
- To be considered for the AI Rubric Designer position, you need at least 3 years of experience in backend engineering, AI automation, or complex systems integration. You should also have a strong command of at least two major programming languages (Python, JavaScript, Go, or Java), experience with SQL databases, and practical experience with live, non-mocked environments and multi-turn system interactions.
- Is the AI Rubric Designer position remote, and where can I work from?
- Yes, the AI Rubric Designer position is fully remote. While the role is remote, candidates must be located in Finland, France, Italy, or Norway.
- How is compensation handled for the AI Rubric Designer role?
- Compensation for the AI Rubric Designer role is hourly, ranging from $30 to $50 USD, depending on your experience and the complexity of the tasks. Payments are processed weekly through platforms like PayPal or AirTM.
- What is OpenClaw, and how is it used in this AI Rubric Designer role?
- OpenClaw is a framework or tool used for building AI agents. In this role, you will explore building agents using OpenClaw across various AI models, and then design rubrics to evaluate the outcomes of these agents.
- What kind of technical feedback is expected for LLM training?
- The technical feedback expected for LLM training involves providing high-density, detailed insights derived from assessing production-grade software architecture and analyzing the multi-turn interactions and behaviors of AI systems. This feedback is crucial for improving the performance and capabilities of Large Language Models.