This a Full Remote job, the offer is available from: New York (USA)
We are sharing a specialised full-time consulting opportunity for experienced QA and test engineers with strong expertise in test-case design, end-to-end debugging, quality assurance, Python, and complex technical evaluation workflows.
This role supports the development of advanced agentic evaluation benchmarks for frontier AI models. Selected professionals will review complex multi-step tasks, test reference solutions, identify ambiguity and grading gaps, debug technical environments, and develop repeatable quality processes that keep benchmark results accurate and trustworthy.
Key Responsibilities
Test Case Design
• Create comprehensive test cases confirming that benchmark tasks function as intended
• Design positive, negative, boundary, and edge-case tests
• Validate task requirements, expected outputs, reference solutions, and grading logic
• Identify scenarios that may produce incorrect or misleading evaluation results
• Ensure tests measure the intended technical capability accurately
Benchmark Task Review
• Review complex multi-step tasks and reference solutions before finalisation
• Identify ambiguous instructions, inconsistent requirements, missing assumptions, and incomplete acceptance criteria
• Run tasks independently to confirm reproducibility and expected behaviour
• Assess whether grading standards are clear, fair, and technically defensible
• Provide actionable feedback to task authors and researchers
Technical Debugging
• Investigate failures across Python scripts, test harnesses, repositories, and task environments
• Diagnose unexpected behaviour within unfamiliar codebases
• Reproduce reported issues and isolate their underlying causes
• Correct or document environment, dependency, logic, and validation problems
• Use Git-based workflows to support structured review and collaboration
Quality Process Development
• Develop practical checklists and repeatable review procedures for benchmark quality
• Improve consistency across task validation, testing, and approval workflows
• Document findings clearly so authors can resolve issues efficiently
• Track recurring defects and recommend preventive quality measures
• Collaborate closely with researchers, task authors, and other technical reviewers
Benchmark Integrity & Shortcut Detection
• Examine AI agent runs for unintended shortcuts, loopholes, and grading weaknesses
• Identify cases where models can receive credit without completing the intended reasoning or technical work
• Test whether benchmark tasks remain robust across alternative approaches
• Strengthen evaluation criteria to maintain reliable and meaningful benchmark scores
• Distinguish valid solution diversity from unintended task exploitation
Ideal Profile
Strong candidates may have:
• At least 1 year of experience in test engineering, quality assurance, software engineering, research engineering, or a related technical role
• Demonstrated experience designing test cases and quality-review processes
• Strong end-to-end debugging skills across complex technical systems
• Working proficiency in Python and Git
• Comfort navigating unfamiliar codebases, repositories, and execution environments
• Exceptional attention to detail and strong written documentation habits
• Ability to identify ambiguity, edge cases, hidden assumptions, and quality gaps
• Capacity to work independently through open-ended technical problems
• Reliable availability for approximately 35 hours per week
Educational Background
• A master's degree or PhD in a STEM field is highly relevant
• Equivalent practical experience in an engineering-intensive or research-intensive domain may also be considered
• Academic or professional experience involving computer science, software engineering, machine learning, mathematics, statistics, or a related technical field may strengthen an application
• Technical research, open-source contributions, testing projects, or substantial engineering work may also be valuable
Nice to Have
• Experience with AI training, model evaluation, or quality review of AI-generated work
• Familiarity with agentic systems and multi-step AI benchmarks
• Background testing machine learning, research, or data-processing workflows
• Experience developing automated test suites or validation scripts
• Familiarity with CI/CD systems, test harnesses, containers, or reproducible environments
• Experience reviewing reference solutions, grading logic, or technical rubrics
• Knowledge of adversarial testing, failure-mode analysis, or benchmark design
• Prior collaboration with AI research or evaluation teams
Why This Opportunity
• Serve as the quality backbone for advanced agentic AI benchmarks
• Ensure complex evaluation tasks are accurate, reproducible, and resistant to shortcuts
• Apply testing and debugging expertise to technically challenging AI research workflows
• Work closely with researchers and task authors on benchmark improvement
• Help protect the reliability of evaluation results for frontier AI systems
• Participate in a structured full-time remote role with competitive hourly compensation
Contract Details
• Full-time W-2 contingent employment opportunity
• Fully remote within the United States
• Expected commitment of approximately 35 hours per week
• Competitive rates between $55–$85 per hour depending on expertise and project scope
• Individual task reviews may require one to two days of focused technical work
• Work may include test design, benchmark review, Python debugging, quality-process development, and shortcut detection
• Close collaboration with research and task-development teams
• Engagement scope and duration may evolve according to project requirements and performance
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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