AI Trainer (Life Sciences) - Remote

Job Description: Computational Engineer – AI Training & Model Evaluation

Role Type: Contractor Location: Global (Fully Remote) Time Commitment: 15+ hours per week Schedule: Flexible

About the Role

We are seeking experienced Computational Engineers with prior professional AI-training or model-evaluation experience to contribute to a project focused on improving advanced AI systems.

You will apply your engineering expertise and programming skills to develop challenging technical tasks, evaluate AI-generated code and analyses, identify errors, and establish objective standards for accurate solutions. This opportunity is intended for professionals with substantive experience in AI training involving engineering or other technical content.

Key Responsibilities

• Create realistic computational engineering tasks that challenge advanced AI models.
• Develop clear task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate AI-generated code, calculations, simulations, designs, analyses, and technical deliverables.
• Identify technical errors, inconsistencies, and limitations in model-generated outputs.
• Validate engineering results using calculations, simulations, measurements, or physical testing.
• Establish objective standards for assessing technical accuracy, reliability, and completeness.

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 skills, 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, reproducible solutions.

Relevant Engineering Specializations

• 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
• Biomedical Engineering

Public Proof of Technical Expertise

Strong applicants should be able to demonstrate their expertise through evidence such as:

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

Note: Confidential employer or customer materials must not be shared.

Preferred Qualifications

• Experience creating AI-training tasks, reference solutions, evaluation rubrics, graders, or benchmark problems.
• Experience reviewing technical submissions from other AI trainers.
• A master's degree, PhD, or substantial professional experience in a relevant engineering discipline.
• Familiarity 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.
• Previous involvement in long-horizon or agentic AI evaluations.
• Availability to begin immediately.

Application Process

- Application Submission: Submit your application along with details of your AI-training experience, engineering specialization, and public technical evidence.

- AI Interview: Complete an AI interview that includes technical and integrity checks.

- Hiring Manager Review: The team evaluates your profile and interview performance.

- Onboarding: Selected candidates complete project onboarding and begin their first assigned task.

Compensation

Compensation is output-based, with payment for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.

Originally posted on Himalayas