AI Trainer (Engineering Sciences)

Pay: $60 - $120/hour

Required Skills
Hardware
Aerodynamics
Mechanical Engineering
Electrical Engineering
AI Training
Programming Skills
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.

Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.

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
Create realistic computational engineering tasks that challenge advanced AI models.
Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
Evaluate model-generated code, calculations, simulations, designs, analyses, and technical deliverables.

Required Qualifications

Prior paid experience training, evaluating, benchmarking, or improving AI systems on engineering or technical tasks.
Deep expertise in at least one relevant engineering specialization.
Strong programming or scripting ability, with Python strongly preferred.
Hands-on experience using computational methods for engineering analysis, simulation, modeling, optimization, testing, automation, or data processing.
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.

Public Proof of Skill

Strong applicants should provide evidence such as:

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

Confidential employer or customer materials should not be shared.

Preferred Qualifications

Experience creating AI-training tasks, reference solutions, rubrics, graders, or benchmark problems.
Experience reviewing other AI trainers’ technical submissions.
A master’s degree, PhD, or substantial professional experience in a relevant engineering discipline.
Experience with command-line tools, scripting interfaces, configuration files, solver APIs, or programmatic engineering software.
Experience validating computational results against calculations, simulations, measurements, or physical tests.
Prior experience with long-horizon or agentic AI evaluations.
Availability to begin immediately.
Application Process
Submit your application, AI-training experience, specialization, and public technical evidence.
Complete an AI interview containing technical and integrity checks.
Complete a hiring-manager review.
If selected, complete project onboarding and begin your first assigned task.

Compensation

Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.
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