Remote/Part-Time Mechanical Engineer

Aston Carter•New York, NY

3d•$125 - $125•Remote

About The Position

This role offers an opportunity for an experienced mechanical engineer or applied science professional to use their real-world technical expertise to train and improve advanced AI systems. You will review AI-generated responses to complex science and engineering prompts, identify errors in reasoning and calculations, design realistic technical problems from your own experience, and write clear, expert-level solutions that serve as training data. This is a fully remote, project-based position focused on rigorous technical analysis, precise written communication, and independent, self-directed work over a 12-week engagement.

Requirements

At least 5+ years of applied science or engineering experience in industry or advanced research, such as mechanical, electrical, civil, materials, energy, chemical, environmental, physics, or similar fields, excluding undergraduate study.
Hands-on practitioner experience, currently or recently performing detailed individual-contributor work rather than working solely in a managerial capacity.
Strong background in mechanical engineering, mechanical design, or closely related engineering disciplines, with the ability to perform rigorous technical analysis and calculations.
Experience with experimental design, including planning, executing, and interpreting experiments or tests.
Proficiency in data analysis, including working with test and measurement data, instrument outputs, and other real-world technical datasets.
Comfort using the analysis tools and software commonly used in your engineering or scientific field.
Strong technical writing skills, with the ability to explain complex engineering and scientific concepts clearly, concisely, and accurately.
Ability to read and follow detailed written instructions carefully and consistently.
Receptiveness to feedback and willingness to revise work through multiple review cycles.
Baseline technology literacy, including comfort with cloud-based file tools such as Google Workspace or similar platforms.
Ability to manage browser profiles, download and install desktop applications, and handle everyday file operations such as converting between Excel and Google Sheets and creating compressed (zipped) files for sharing.
Ability to work independently and effectively in a remote, self-directed environment.
Availability to commit approximately 30–40 hours per week over the full 12-week engagement.
Current residence within the United States.

Nice To Haves

Advanced degree in a relevant engineering or scientific discipline is beneficial, though practical expertise is emphasized over formal credentials.
Professional licensure such as Professional Engineer (PE) or similar certifications is a plus.
Experience in quality engineering, including developing or applying standards, criteria, or rubrics to assess technical work.
Familiarity with data visualization tools and techniques to interpret and present technical data clearly.
Exposure to predictive modeling, simulations, or other computational analysis methods used in engineering and applied science.
Experience in process analysis, testing, measurement, modeling, or simulation work that involves hands-on interaction with technical systems and data.
Interest in AI, machine learning, or data-driven systems, particularly in the context of improving technical accuracy and reliability.
Enthusiasm for applying engineering and scientific expertise in a non-traditional role focused on training and evaluating AI systems.

Responsibilities

Review and evaluate AI-generated responses involving scientific reasoning, engineering analysis, calculations, experimental design, and technical explanations for correctness, completeness, and rigor.
Identify factual errors, flawed assumptions, statistical traps, measurement issues, and incomplete or inconsistent reasoning in AI-generated content.
Design realistic, challenging technical problems and scenarios based on your professional mechanical engineering or broader applied science and engineering experience.
Write clear, technically rigorous, expert-level solutions and explanations that can be used as high-quality AI training examples.
Grade AI-generated work against detailed evaluation criteria and rubrics, assessing accuracy, clarity, and adherence to technical standards.
Flag errors, ambiguous statements, and flawed reasoning in model outputs and provide specific, constructive corrective feedback and guidance.
Run assigned tasks through AI models, review outputs, and iteratively revise work based on feedback from expert reviewers.
Follow detailed written instructions precisely, including documentation standards, formatting rules, and file-upload requirements.
Maintain consistent quality, accuracy, and productivity throughout the engagement, meeting throughput expectations over the 12-week period.
Complete long-form, independent assignments that may require several hours of focused, uninterrupted work.
Work with real-world technical data such as test and measurement datasets, instrument outputs, and engineering design or simulation files as needed for problem creation and solution development.
Use data analysis, data visualization, predictive modeling, and quality engineering practices where relevant to evaluate or construct technical examples.