AI Trainer (Mathematics & Statistics) - Remote

Job Description: Computational Physical Scientist – 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 Physical Scientists with prior professional AI-training or model-evaluation experience to contribute to a project focused on improving advanced AI systems. You will apply your scientific 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 AI-training experience involving physical-science content. Key Responsibilities - Create realistic computational physical-science tasks that challenge advanced AI models. - Develop clear task instructions, reproducible environments, reference solutions, and evaluation criteria. - Evaluate AI-generated calculations, code, simulations, models, and technical deliverables. - Identify invalid assumptions, numerical errors, physically impossible results, incorrect methodologies, and reproducibility failures. - Assess the scientific accuracy, reliability, and completeness of model-generated outputs. - Establish objective standards for evaluating computational and scientific solutions. 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 physical-science specialization. - Hands-on experience using computational methods for modeling, simulation, numerical analysis, data analysis, or reproducible research. Relevant Specializations - Physics - Astronomy and Cosmology - Materials Science - Chemistry - Computational Physics - Computational Chemistry - Scientific Simulation Public Proof of Technical Expertise Strong applicants should be able to demonstrate their expertise through evidence such as: - Peer-reviewed publications or preprints involving computational methods. - Public simulation or scientific analysis code. - Google Scholar, ORCID, arXiv, or equivalent research profiles. - Open-source scientific software contributions. - Reproducible notebooks, models, datasets, or simulations. - Public benchmarks, experiments, or technical projects. Preferred Qualifications - Experience creating AI-training tasks, reference answers, evaluation rubrics, graders, or benchmark problems. - Familiarity with NumPy, SciPy, SymPy, Astropy, PyTorch, JAX, or comparable scientific tools. - Experience validating simulations or models against analytical results, observations, or experimental data. - Availability to begin immediately. Application Process - Application Submission: Submit your application along with details of your AI-training experience, scientific 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