Remote AI Media Tech Trainer (Contract)

LinkedIn; United States; Remote; USD 103,000 - 207,000; Part time

Job summary

LinkedIn is seeking an experienced AI Trainer project consultant to craft terminal‑based evaluation tasks within a Docker‑driven workflow. You will design challenging problems, create reproducible environments, and build reference solutions with automated tests to ensure robust evaluation of AI systems.

This part‑time, project‑based role emphasizes hands‑on programming, testing, and edge‑case analysis, with the freedom to manage your own schedule and tools while contributing to high‑quality AI

Qualifications

• 5+ years of hands‑on engineering experience.
• Deep expertise in design engineering, audio/DSP, or computational media.
• Strong hands‑on programming ability in relevant languages and tools.
• Experience building software, tooling, processing pipelines, automation, or computational systems in creative domains.
• Ability to independently implement and validate technical problems you design.
• Strong debugging and automated testing ability.
• Ability to translate creative or media requirements into objective, machine‑verifiable success criteria.
• Familiarity with Git and GitHub workflows; Docker-based environments; CI/CD workflows.

Responsibilities

• Design technically challenging but fair tasks grounded in relevant engineering disciplines.
• Translate requirements into precise, self‑contained task specifications.
• Build reproducible, containerized task environments using Docker.
• Implement complete reference solutions with appropriate languages and frameworks.
• Develop hidden tests or verifiers that assess actual correctness.
• Validate that reference solutions pass while task environments remain robust.

Skills

Hands-on engineering

Programming proficiency

Automated testing

Debugging

Git & GitHub workflows

Tools

Docker

Git

GitHub

CI/CD workflows

Job description

LinkedIn is seeking an experienced AI Trainer project consultant to craft terminal‑based evaluation tasks within a Docker‑driven workflow. You will design challenging problems, create reproducible environments, and build reference solutions with automated tests to ensure robust evaluation of AI systems.

This part‑time, project‑based role emphasizes hands‑on programming, testing, and edge‑case analysis, with the freedom to manage your own schedule and tools while contributing to high‑quality AI