Research Scientist, LLM Evaluation – Post-Training
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