Machine Learning Engineer AI Trainer, $100/hr

Project Overview:

Join a growing community of professionals advancing the next wave of AI. As an AI Trainer, you’ll play a hands-on role by analyzing and providing feedback on data to improve LLM performance, helping ensure that the next generation of AI technology is accurate and trustworthy.

We are seeking a group of skilled ML / Research / Systems Engineers to work as a project consultants in our AI Labor Marketplace. This is not a full-time employment position — you will be engaged as an expert project consultant on a contract basis.

Location: U.S.-based experts only

Engagement: Part-time, project-based expert evaluation work

Work Type: Remote

Project Summary:

Contributors will create technically challenging Terminal-Bench 4.0 evaluation tasks designed to test an AI system on realistic terminal-based engineering work. Each task requires end-to-end ownership: defining a difficult problem, creating a reproducible Docker environment, writing precise instructions, developing a reference solution, building robust hidden tests, and validating that the reference passes while the untouched task fails. This is benchmark engineering rather than conventional coding-problem creation.

Consultant Engagement Terms:

This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors or employers (subject to those vendors’ allowances).

Responsibilities:

Contributors will:

• Design challenging, technically substantive terminal-based engineering tasks.
• Build reproducible Docker environments for each task.
• Write clear, self-contained task specifications and instructions.
• Implement correct, working reference solutions.
• Develop hidden test suites that evaluate genuine functional correctness.
• Design evaluations that resist hard-coded answers, shortcuts, and incomplete solutions.
• Verify that the reference solution passes the evaluation.
• Verify that the untouched starting task fails the evaluation and debug reproducibility or grading issues as needed.

Expected Outcomes:

• A complete, reproducible Terminal-Bench 4.0 task.
• A working Docker-based task environment.
• Clear task instructions.
• A validated reference solution.
• A robust hidden test suite.
• Evidence that the reference solution passes and the untouched task fails.

Qualifications:

• Strong hands-on software engineering ability, particularly in Python.
• Professional or research experience in ML engineering, research engineering, AI systems, systems engineering, GPU programming, or closely related technical work.
• Fluency working in Linux/terminal environments.
• Experience with Docker and reproducible development environments.
• Strong software testing and debugging skills, including the ability to anticipate edge cases and unintended solution paths.
• Ability to independently investigate difficult technical problems and produce reliable, maintainable implementations.
• For GPU/accelerated-computing tasks, relevant experience with GPU kernels, CUDA, Triton, performance optimization, or comparable low-level technologies is highly desirable.

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