LMArena is looking for a Machine Learning Scientist to help advance how AI models are evaluated and understood, focusing on their real-world performance, trustworthiness, and capability through human preference signals. The goal is to contribute to the scientific foundations of understanding AI at scale and inform both public leaderboards and tools for model developers.
Requirements
- Hands-on experience training large-scale models, including reward models, preference models, and fine-tuning LLMs with methods like RLHF, DPO, and contrastive learning.
- Strong foundation in ML and statistics, with a track record of designing novel training objectives, evaluation schemes, or statistical frameworks to improve model reliability and alignment.
- Fluent in the full experimental stack, from dataset design and large-batch training to rigorous evaluation and ablation, with an eye for what scales to production.
- Strong understanding of LLMs and modern deep learning architectures (e.g., Transformers, diffusion models, reinforcement learning with human feedback)
- Proficiency in Python and ML research libraries such as PyTorch, JAX, or TensorFlow
- Demonstrated ability to design and analyze experiments with statistical rigor
- Experience publishing research or working on open-source projects in ML, NLP, or AI evaluation
Responsibilities
- Design and conduct experiments to evaluate AI model behavior across reasoning, style, robustness, and user preference dimensions
- Develop new metrics, methodologies, and evaluation protocols that go beyond traditional benchmarks
- Analyze large-scale human voting and interaction data to uncover insights into model performance and user preferences
- Collaborate with engineers to implement and scale research findings into production systems
- Prototype and test research ideas rapidly, balancing rigor with iteration speed
- Author internal reports and external publications that contribute to the broader ML research community
- Partner with model providers to shape evaluation questions and support responsible model testing
Other
- Deeply collaborative mindset, working closely with engineers to productionize research insights and iterating with product teams to align modeling goals with user needs.
- Ability to translate research questions into practical systems and collaborate across engineering and product teams
- PhD or equivalent research experience in Machine Learning, Natural Language Processing, Statistics, or a related field
- Comfortable working with real-world usage data and designing metrics beyond standard benchmarks
- Passion for open science, reproducibility, and community-driven research.
