AI Trainer – Life Sciences - Remote

AI Trainer – Life Sciences - Remote

Location: Global
Work Arrangement: Fully Remote
Employment Type: Independent Contractor
Time Commitment: 15+ hours per week
Schedule: Flexible
Compensation: $115,200–$230,400/year equivalent
Hourly Equivalent: $60–$120/hour, paid per accepted task

Annualized compensation is an illustrative equivalent based on 40 hours/week × 48 weeks/year. This is an output-based engagement, not a guaranteed salary. Payment is determined by accepted tasks that meet project specifications and quality requirements. Actual earnings depend on task availability, completion time, and approved work.

About the Role

We are seeking experienced Computational Life Scientists with prior professional AI-training or model-evaluation experience to contribute to the development and improvement of advanced AI systems.

In this fully remote contractor role, you will apply your scientific expertise and programming skills to create challenging computational tasks, evaluate AI-generated code and analyses, identify scientific and technical errors, and establish objective standards for accurate solutions.

Your work will focus on life-science and biomedical applications, helping assess how AI models interpret complex biological information, conduct computational analyses, and solve research problems. This opportunity is intended for professionals with substantive experience training or evaluating AI systems on scientific or technical content.

Key Responsibilities

• Create realistic computational life-science tasks that challenge advanced AI models.

• Develop clear task instructions, reproducible computational environments, reference solutions, and evaluation criteria.

• Evaluate AI-generated code, scientific analyses, computational models, experiments, and technical deliverables.

• Identify invalid biological assumptions, methodological errors, data leakage, statistical issues, and reproducibility failures.

• Assess the scientific accuracy, reliability, and methodological rigor of model-generated outputs.

• Apply domain expertise to establish objective standards for evaluating computational and scientific solutions.

• Provide detailed technical feedback to support improvements in AI model performance across life-science applications.

Required Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.

• Strong practical proficiency in Python.

• Deep expertise in at least one relevant life-science specialization.

• Hands-on experience applying computational methods to modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.

• Strong analytical reasoning and scientific problem-solving skills.

• Ability to evaluate complex scientific outputs and identify biological, statistical, methodological, and computational errors.

Relevant specializations include:

• Biology and computational biology

• Bioinformatics

• Medicine and healthcare

• Biomedical science

• Neuroscience

• Genomics

• Ecology

• Evolutionary biology

Public Proof of Technical Expertise

Applicants should be prepared to provide verifiable evidence of their scientific and computational work, such as:

• Peer-reviewed publications or preprints involving computational methods.

• Public research code or bioinformatics pipelines.

• Research profiles such as Google Scholar or ORCID.

• PhysioNet or other biomedical competition results.

• Contributions to open-source scientific software.

• Public datasets, computational models, notebooks, or technical projects.

Preferred Qualifications

• Experience creating AI-training tasks, reference answers, evaluation rubrics, automated graders, or benchmark problems.

• Experience reviewing and assessing technical submissions from other AI trainers.

• Familiarity with scientific computing and machine-learning tools such as NumPy, SciPy, pandas, Biopython, PyTorch, and JAX, or comparable technologies.

• Previous experience with long-horizon or agentic AI evaluations.

• Availability to begin immediately.

Application and Selection Process

Candidates should submit an application outlining their previous AI-training or model-evaluation experience, scientific specialization, and relevant public technical evidence.

The selection process includes:

1. Application Review: Assessment of AI-training experience, domain expertise, and public technical evidence.

2. AI Interview: Technical evaluation incorporating integrity checks.

3. Hiring Manager Review: Further assessment of qualifications and project suitability.

4. Project Onboarding: Selected candidates complete onboarding before beginning their first assigned task.

Compensation Structure

This is an output-based independent contractor engagement. Experts are compensated for tasks that meet project specifications and quality requirements. Task availability and completion time may vary, so earnings depend on accepted deliverables rather than guaranteed working hours.

Life-science professionals with demonstrated AI-training experience, strong Python skills, and verifiable computational research expertise are encouraged to apply.