Hardware Engineering AI Trainer, $75-$100/hour

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 Hardware Engineering Domain 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 hardware engineering tasks used to evaluate advanced AI systems. Relevant specialties include RTL/digital design, CAD, EDA, and computational or automated hardware-design workflows.

Each contributor will own their task end to end: defining a difficult but fair hardware engineering problem, constructing a reproducible Docker environment, implementing a correct reference solution, and creating hidden tests or verifiers that distinguish genuinely correct solutions from incomplete, hard-coded, or superficially valid implementations.

This work is intended for experienced hardware, RTL, CAD, and EDA engineers who combine strong domain expertise with substantial hands-on coding. Candidates should be comfortable writing RTL, software, scripts, verification logic, or automation using the languages and APIs relevant to their specialty.

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 hardware engineering problems grounded in their area of specialization.
• Write precise, self-contained task specifications with objective success criteria.
• Build reproducible, containerized task environments using Docker.
• Implement complete and correct reference solutions using the appropriate RTL, programming, scripting, CAD, or EDA tools.
• Develop robust hidden tests or verifiers that assess actual correctness.
• Account for important edge cases, constraints, failure modes, and plausible-but-incorrect implementations.
• Anticipate ways an AI agent might satisfy superficial checks without correctly completing the underlying engineering task.
• Validate that the reference solution passes while the untouched task environment fails.

Expected Outcomes:

• Complete terminal-based hardware engineering 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 solution passes and the baseline task does not.

Qualifications:

• 5+ years of relevant hands-on hardware engineering experience.
• Deep expertise in at least one applicable area, including RTL/digital design, CAD, EDA, hardware verification, or computational hardware-design workflows.
• Strong hands-on coding ability using languages and tools relevant to the candidate's specialty.
• For RTL-oriented candidates, substantial experience implementing or verifying digital designs using appropriate hardware-description and programming languages.
• For CAD/EDA-oriented candidates, substantial experience scripting, automating, or extending engineering workflows using programming languages, CAD/EDA APIs, or other computational tools.
• Ability to independently implement and validate the difficult technical problems you design.
• Strong debugging, testing, and verification skills.
• Ability to translate hardware constraints and expected behavior into objective, machine-verifiable criteria.
• Experience with Git and GitHub workflows.
• Experience building or working with Docker-based environments.
• Familiarity with CI/CD or automated build and test workflows.
• Strong reasoning about edge cases, failure modes, and alternative implementations.
• Ability to design tests that reject solutions that appear valid but violate underlying functional or engineering requirements.

LinkedIn Data Security and Privacy Notice: https://legal.linkedin.com/content/dam/legal/Global-Data-Privacy-Notice_LI_TalentCommunity.pdf