Remote | Symbolic AI Planning QA Review Consultant — $45–$65/hour

24-Mag•New York, NY

1d•Remote

About The Position

We are sharing a specialised part-time consulting opportunity for professionals experienced in automated planning, symbolic AI, computer science research, artificial intelligence, machine learning, operations research, technical quality review, and rigorous problem validation. This role supports current and upcoming remote consulting opportunities focused on AI reasoning evaluation, technical QA review, automated planning problem validation, solution verification, and high-quality data assessment. Selected professionals will review challenging symbolic AI and automated planning problems authored by domain experts, verify correctness and clarity, assess technical rigor, and provide structured feedback based on detailed evaluation criteria.

Requirements

Advanced expertise in automated planning, symbolic AI, artificial intelligence, machine learning, computer science, operations research, or a closely related field
Strong reviewing and QA instincts with the ability to rigorously verify problem correctness and solution quality
High attention to detail and ability to identify subtle technical issues, ambiguity, or reasoning gaps
Exceptional written communication skills and ability to explain technical feedback clearly
Comfort applying structured rubrics, evaluation criteria, and project-specific guidelines consistently
Ability to work independently in a remote, asynchronous, project-based environment
Strong judgment when assessing technical rigor, correctness, and clarity in advanced computer science problems
PhD, advanced degree, or equivalent research experience in a relevant technical field is preferred
Relevant fields may include Computer Science, Artificial Intelligence, Machine Learning, Operations Research, automated planning, symbolic AI, formal methods, or related disciplines
Academic or professional experience involving AI research, planning systems, algorithm design, optimization, technical review, or domain-specific evaluation is highly relevant
Experience with peer review, graduate-level teaching, research assessment, technical QA, or advanced problem validation may be especially valuable

Nice To Haves

Experience reviewing research problems, technical prompts, exam questions, formal reasoning tasks, or advanced computer science assessments
Familiarity with planning algorithms, symbolic reasoning, search methods, PDDL-style planning, constraint-based reasoning, or operations research methods
Experience verifying solutions, checking proofs or reasoning chains, identifying edge cases, or improving technical problem clarity
Comfort reviewing multiple independent submissions and maintaining consistent quality standards
Strong ability to identify methodological issues, incorrect assumptions, weak constraints, or unsupported technical conclusions

Responsibilities

Review technical problems involving automated planning, symbolic AI, search, reasoning systems, planning representations, or operations research concepts
Verify the correctness, clarity, and rigor of problem statements, assumptions, expected solutions, and evaluation logic
Identify ambiguous wording, incomplete constraints, flawed reasoning, incorrect solutions, or missing technical details
Support multi-review QA workflows where each problem may receive independent technical review from multiple experts
Evaluate whether proposed solutions, reference answers, or reasoning paths are technically sound and domain-appropriate
Assess AI-related technical content for accuracy, completeness, consistency, and research-level quality
Review outputs involving formal reasoning, algorithmic logic, planning methods, symbolic representations, and domain-specific problem-solving
Identify unsupported conclusions, methodological gaps, weak explanations, or inconsistencies between problem requirements and solutions
Provide clear, precise, and well-supported written feedback on technical problems and solutions
Explain review decisions with consistency, attention to detail, and domain-specific judgment
Follow detailed task instructions and apply QA criteria accurately across review assignments
Participate in screening, onboarding, calibration, or domain alignment activities when required