Media Technology AI Trainer, $75-$100/hour

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

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 skilled Creative Technology Engineering Expert to work as a project consultant 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 challenging, terminal‑based media technology tasks used to evaluate advanced AI systems. Relevant specialties include design engineering, design systems, audio engineering, digital signal processing (DSP), music technology, and other computational creative workflows.

Each contributor owns their task end to end: defining a difficult but fair problem within their specialty, creating a reproducible Docker environment, implementing a correct reference solution, and building hidden tests or verifiers that reliably distinguish correct solutions from shortcuts, partial implementations, and plausible‑looking but incorrect results.

This work is intended for creative‑technical specialists who are also strong hands‑on programmers. Contributors must be able to translate design, audio, or media requirements into code and objective evaluation criteria. Purely creative or visual expertise without substantial programming ability is not sufficient.

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 technically challenging but fair tasks grounded in design engineering, audio/DSP, music technology, or related computational media disciplines.
• Translate creative and technical requirements into precise, self‑contained task specifications.
• Build reproducible, containerized task environments using Docker.
• Implement complete and correct reference solutions using the appropriate programming languages, frameworks, APIs, or domain‑specific tools.
• Develop robust hidden tests or verifiers that assess actual correctness rather than superficial output.
• Account for relevant constraints, edge cases, numerical behavior, and plausible‑but‑incorrect implementations.
• Anticipate how an AI agent could satisfy simple checks without correctly solving the underlying technical problem.
• Validate that the reference solution passes while the untouched task environment fails.

Expected Outcomes:

• Complete terminal‑based media technology evaluation tasks.
• Clear and reproducible task specifications.
• Working reference implementations.
• Dockerized environments containing required dependencies and configuration.
• Automated hidden test suites or robust correctness verifiers.
• Validation demonstrating that the reference implementation passes and the baseline task does not.

Qualifications:

• 5+ years of relevant hands‑on engineering experience.
• Deep expertise in at least one applicable area, such as design engineering, design systems, audio engineering, digital signal processing, music technology, or computational media.
• Strong hands‑on programming ability in the languages and tools relevant to the candidate's specialty.
• Demonstrated experience building software, technical tooling, processing pipelines, automation, or computational systems within a creative or media domain.
• Ability to independently implement and validate the technical problems you design.
• Strong debugging and automated testing ability.
• Ability to translate creative or media requirements into objective, machine‑verifiable success criteria.
• Experience with Git and GitHub workflows.
• Experience building or working with Docker‑based environments.
• Familiarity with CI/CD or automated build/test workflows.
• Strong understanding of edge cases, constraints, failure modes, and alternative implementations.
• Ability to design tests that reject superficially convincing outputs that do not satisfy the underlying technical requirements.

LinkedIn Data Security and Privacy Notice:

https://legal.linkedin.com/content/dam/legal/Global-Data-Privacy-Notice_LI_TalentCommunity.pdf