• Evaluate and annotate scientific data, equations, and experimental findings across various physics disciplines. • Review technical content for scientific accuracy, clarity, completeness, and logical consistency. • Provide expert feedback to improve the quality of physics-related content used in AI training. • Create and review problem sets, technical explanations, research summaries, and analytical write-ups covering both foundational and advanced physics concepts. • Develop clear, well-structured documentation that enables AI models to better understand scientific principles and reasoning. • Author or edit experimental descriptions and technical reports with precision and attention to detail. • Assess AI-generated responses for technical correctness, logical reasoning, and scientific rigor. • Identify inaccuracies, ambiguities, or gaps in AI-generated physics content and provide actionable recommendations for improvement. • Contribute to the creation and refinement of high-quality datasets used for AI training and benchmarking. • Work collaboratively with project managers and subject matter experts in a remote environment. • Identify opportunities to improve data quality, content coverage, and evaluation methodologies. • Ensure timely delivery of high-quality work aligned with project milestones and quality standards.

• Review, solve, and evaluate advanced mathematical problems across pure and applied mathematics. • Develop accurate, well-structured mathematical proofs, derivations, technical explanations, and analytical solutions. • Ensure mathematical rigor, precision, and logical consistency in all deliverables. • Assess AI-generated mathematical solutions for correctness, clarity, completeness, and logical reasoning. • Identify errors, inconsistencies, or gaps in AI outputs and provide actionable feedback to improve model performance. • Validate mathematical reasoning against established theories and best practices. • Create high-quality mathematical datasets, annotations, and domain-specific training content for AI model development. • Produce clear technical documentation and structured explanations covering a wide range of mathematical concepts. • Contribute to benchmark datasets used for evaluating AI reasoning capabilities. • Collaborate remotely with cross-functional teams and subject matter experts across various mathematical disciplines. • Communicate complex mathematical concepts effectively to both technical and non-technical audiences. • Document methodologies, findings, and recommendations in a clear and organized manner.

• Analyze and evaluate complex computer science problems, solutions, and methodologies across various domains. • Assess technical approaches and recommend well-structured, scalable, and efficient solutions based on industry best practices. • Ensure technical accuracy, logical consistency, and clarity in all deliverables. • Author, review, and refine technical documentation, including system design documents, research papers, technical specifications, and architecture write-ups. • Develop real-world computer science scenarios, datasets, and case studies to support AI model training and evaluation. • Create structured technical explanations that improve AI understanding and reasoning. • Review AI-generated responses for technical correctness, completeness, and adherence to computer science principles. • Identify inaccuracies, inconsistencies, and opportunities for improvement while providing constructive feedback. • Support the evaluation and continuous enhancement of AI-driven tools through domain expertise. • Work closely with project coordinators and subject matter experts to refine task guidelines and improve data quality. • Provide clear written and verbal feedback to enhance AI model relevance, robustness, and performance. • Collaborate in a remote environment while maintaining high-quality deliverables and documentation.

• Build the automation layer. Integrate our ad platforms, CRM and analytics. Build automated monitoring, diagnostics, decision support and action workflows that cut the team's recurring manual work. Anything that touches spend keeps a human in the loop: the system proposes, a person approves. • Guarantee the data is right. Read and verify the SQL and data models the systems rely on, including ones other people wrote. Judge whether each metric is actually computed the way we think it is, and catch the measurement issues before they drive a bad automated decision. • Get this working for Performance Marketing across our 9 markets first, then it becomes the blueprint we roll out to the rest of the company.