Physics PhD - AI Trainer

Summary

Join RYZ Labs as a highly skilled Physicist to leverage your expertise in physics, advanced mathematics, and analytical reasoning to train, fine-tune, and rigorously evaluate AI models. You will design evaluation frameworks for AI systems in complex physical environments, research optimal AI behaviors, conduct in-depth testing of AI components, develop scoring rubrics, and document findings. This role requires a Ph.D. in Physics or a related field, a strong foundation in scientific research methods, and experience with programming languages like Python, MATLAB, or Julia. Excellent communication skills are essential. Preferred qualifications include experience with AI/ML frameworks, large datasets, and computational physics.

Requirements

• Ph.D. in Physics or a closely related field (e.g., Applied Physics, Astrophysics, Mathematical Physics, Engineering Physics)
• Solid foundation in scientific research methods, statistical analysis, and data interpretation
• Strong critical thinking and problem-solving skills, particularly with complex, multi-variable systems
• Experience with programming languages such as Python, MATLAB, or Julia, particularly for data analysis or modeling
• Excellent communication skills, with the ability to explain complex concepts to non-expert audiences

Responsibilities

• Design sophisticated evaluation frameworks that challenge AI systems in simulations of complex physical environments, focusing on adaptive learning, physical realism, and system response to real-world variables
• Research, define, and validate optimal AI behaviors in physical modeling by analyzing experimental data, computational simulations, peer-reviewed research, and domain-specific case studies
• Conduct in-depth, iterative testing of AI components such as physics-based simulations, predictive modeling engines, and adaptive systems, identifying inaccuracies, points of failure, and opportunities for enhanced fidelity
• Develop robust scoring rubrics and evaluation matrices to consistently assess AI performance across scientific accuracy, predictive reliability, adaptability, and alignment with established physical principles
• Document and report findings through comprehensive feedback cycles, providing actionable insights to refine AI models and guide future development in physics-driven AI systems

Preferred Qualifications

• Hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch)
• Experience working with large datasets and knowledge of data preprocessing techniques
• Background in computational physics or numerical simulation
• Prior experience in interdisciplinary research teams involving AI applications