ComputerSciencePhD

## Accountabilities Evaluate AI-generated outputs for factual accuracy, technical depth, reasoning quality, and relevance across machine learning and AI domains Design, craft, and answer advanced technical prompts related to AI, ML, and adjacent scientific fields Compare and rank multiple AI-generated responses based on correctness, completeness, clarity, and logical consistency Identify hallucinations, errors, inconsistencies, and weaknesses in model reasoning and provide structured feedback Contribute insights and evaluations that directly improve model training, alignment, and performance Apply deep domain expertise to assess complex topics in machine learning, statistics, optimization, and related areas Support continuous improvement of AI systems through high-quality analytical and written feedback Requirements: PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, or a closely related field Strong background in machine learning concepts including model training, evaluation, optimization, and real-world deployment Prior experience as an ML Engineer, AI Researcher, Applied Scientist, Data Scientist, or similar technical role Excellent analytical reasoning skills with strong attention to detail and scientific rigor Ability to clearly articulate complex technical concepts in fluent written English Strong understanding of AI subfields such as NLP, computer vision, reinforcement learning, or data science methodologies Demonstrated ability to critically evaluate technical outputs and identify subtle errors or inconsistencies Comfortable working independently in a remote, flexible, and research-oriented environment Benefits: Highly competitive compensation of USD 150 per hour Fully remote and flexible work arrangement Opportunity to directly contribute to the improvement of advanced AI systems Exposure to state-of-the-art machine learning research and evaluation workflows Flexible workload suitable for academic or profess