Job Description:

• Define and execute a rigorous research agenda focused on LLM evaluation and post-training, with emphasis on evaluation-driven model improvement
• Design experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes
• Develop and validate comprehensive evaluation frameworks for LLM and multimodal systems
• Lead research on frontier evaluation domains including long-context, cross-modal, and dynamic multi-turn evaluations
• Analyze model behavior and failure patterns; generate actionable recommendations for model improvement
• Partner with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies

Requirements:

• MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative field (PhD strongly preferred)
• 5+ years of relevant experience in applied ML research or research science, with substantial work in LLMs or foundation models (graduate research counts)
• Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research
• Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems
• Strong Python coding skills for research experimentation, data processing, evaluation pipelines, statistical analysis, and visualization
• Hands-on experience with modern ML frameworks (PyTorch, Hugging Face, JAX/TensorFlow)

Benefits:

• Remote work options
• Professional development opportunities