Machine Learning Experts: Linear Algebra Exam Audit

What We're Researching

We're hiring machine learning experts to evaluate technical assessment materials used for training and testing purposes. This paid study focuses on validating a set of intermediate linear algebra questions formatted in JSON. Your expertise will help us ensure that the exam accurately measures core competencies like eigenvalue properties, matrix operations, and principal component analysis.

How It Works

During this remote task, you will receive a JSON file containing multiple-choice, short-answer, and coding questions related to linear algebra. You will audit all options and correct answers for technical accuracy while eliminating any ambiguous distractor choices. For the coding questions, you will verify the prompts, refine the rubrics, and write additional edge test cases to ensure robustness. Finally, you will submit the corrected and approved JSON file for our review.

Who This Is For

We are looking for advanced machine learning practitioners with a strong academic or applied background in mathematics. We welcome Machine Learning Engineers, Data Scientists, AI Researchers, and University Instructors who teach linear algebra or data science. You must be comfortable reviewing technical JSON files and writing Python code using NumPy for matrix operations.

Responsibilities

• Audit multiple-choice options and correct answers for technical accuracy
• Identify and eliminate ambiguous distractor choices in the exam questions
• Verify coding question prompts and grading rubrics for completeness
• Write and append edge test cases for NumPy matrix operations
• Format and return the final corrected exam in a valid JSON structure

Requirements

• Professional experience as a Machine Learning Engineer, Data Scientist, or related role
• Deep technical understanding of intermediate linear algebra concepts (PCA, SVD, eigenvalues)
• Proficiency in Python and NumPy for matrix manipulation and pseudoinverse calculations
• Comfortable reading, editing, and validating structured JSON data