AI Trainer (Engineering Sciences)

Pay: $60-$120 per hour (USD).

Pay: $60–$120/hour equivalent, paid per accepted task

Location: Global, fully remote

Job Type: Contractor

Time Commitment: 15+ hours per week

Schedule: Flexible

micro1 is looking for Computational Engineers with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.

You will use your engineering expertise and programming skills to create challenging technical tasks, evaluate model-generated code and analyses, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have completed substantive AI-training work involving engineering or other technical content.

What You’ll Work On

1. Create realistic computational engineering tasks that challenge advanced AI models.
2. Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
3. Evaluate model-generated code, calculations, simulations, designs, analyses, and technical deliverables.
4. Identify invalid assumptions, incorrect methods, numerical issues, physically implausible results, or reproducibility failures.
5. Use programming and relevant engineering tools to solve and validate technical problems.
6. Determine whether results are computationally correct and consistent with engineering principles.

Required Qualifications

1. Prior paid experience training, evaluating, benchmarking, or improving AI systems on engineering or technical tasks.
2. Deep expertise in at least one relevant engineering specialization.
3. Strong programming or scripting ability, with Python strongly preferred.
4. Hands-on experience using computational methods for engineering analysis, simulation, modeling, optimization, testing, automation, or data processing.
5. Experience creating or validating technical results with clear and reproducible answers.

Relevant specializations may include mechanical and aerospace engineering, electrical and computer engineering, electronics and embedded systems, chemical and process engineering, civil and structural engineering, manufacturing and industrial engineering, robotics and controls, energy systems, and biomedical engineering.

Candidates may specialize deeply in one area. Expertise across multiple engineering disciplines is not required.

Public Proof of Skill

Strong applicants should provide evidence such as:

1. Designs taken into production
2. Simulations validated against test data
3. Complex engineering failures personally diagnosed
4. Patents, publications, or technical reports
5. Public engineering code, scripts, models, or simulations
6. Open-source hardware or engineering-software contributions
7. Engineering competition results
8. Reproducible notebooks, benchmarks, or technical projects

Confidential employer or customer materials should not be shared.

Preferred Qualifications

1. Experience creating AI-training tasks, reference solutions, rubrics, graders, or benchmark problems.
2. Experience reviewing other AI trainers’ technical submissions.
3. A master’s degree, PhD, or substantial professional experience in a relevant engineering discipline.
4. Experience with command-line tools, scripting interfaces, configuration files, solver APIs, or programmatic engineering software.
5. Experience validating computational results against calculations, simulations, measurements, or physical tests.
6. Prior experience with long-horizon or agentic AI evaluations.
7. Availability to begin immediately.