Machine Learning Engineer AI Trainer, $100/hr

LinkedIn; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

LinkedIn is seeking part-time, project-based experts to design and evaluate Terminal-Bench 4.0 tasks for AI systems. You will create end-to-end tasks, build Docker environments, and author precise instructions plus robust reference solutions.

You will also develop hidden tests to ensure genuine correctness and resist shortcut solutions, all while maintaining reproducible setups. The role emphasizes hands-on software engineering, Python proficiency, and Linux/Docker fluency, with additional value

Qualifications

• Strong hands-on software engineering ability, especially Python.
• Experience in ML engineering, research engineering, AI systems, or related fields.
• Fluency with Linux/terminal environments and Docker for reproducible setups.
• Experience with GPU programming and performance optimization is a plus.

Responsibilities

• 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 and that the untouched task fails.

Skills

Python

Linux/terminal

Software testing

Debugging

Independent problem solving

Tools

Docker

CUDA

Job description

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

• 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.