Machine Learning Experts: Linear Algebra Exam Audit
- Role
- AI / ML
- Employment
- Contract
Open to US only. Set where you work from to check your eligibility.
No BS summary
This is a paid, one-time remote task for advanced machine learning practitioners with a strong mathematics background. The role involves auditing linear algebra exam materials in JSON format, verifying technical accuracy, and writing Python/NumPy code for edge test cases.
Core skills
Required skills
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.
WHAT YOU'LL DO
- 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
WHO SHOULD APPLY
- 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
COMPENSATION
$11 one-time
READY TO PARTICIPATE?
ABOUT TERAC
Terac is building the world's largest pool of vetted human experts for AI. Researchers, AI labs, and product teams use Terac to recruit, screen, and pay study participants across industries, languages, and skill sets.
Learn more at terac.youtube.com/@jointerac.
What you'll do
- 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
What they require
- 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
- Advanced machine learning practitioners with a strong academic or applied background in mathematics
- Machine Learning Engineers, Data Scientists, AI Researchers, and University Instructors who teach linear algebra or data science
- Must be comfortable reviewing technical JSON files and writing Python code using NumPy for matrix operations
Terac is building the world's largest pool of vetted human experts for AI. Researchers, AI labs, and product teams use Terac to recruit, screen, and pay study participants across industries, languages, and skill sets.