Machine Learning Scientist - Open Source Lead

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
• Communicate results with the broader research community via academic papers, educational content, conference talks
• Collaborate with engineers to implement and scale research findings into production systems
• Prototype and test research ideas rapidly, balancing rigor with iteration speed
• Partner with model providers to shape evaluation questions and support responsible model testing
• Contribute to the scientific integrity and transparency of the LMArena leaderboard and tools

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
• Deeply collaborative mindset, working closely with engineers to productionize research insights and iterating with product teams to align research with user needs
• Comfortable being a visible representative of Arena Intelligence, engaging openly with the research community, and building a strong personal brand to help shape AI research culture
• PhD or equivalent research experience in Machine Learning, Natural Language Processing, Statistics, or a related field
• 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
• Comfortable working with real-world usage data and designing metrics beyond standard benchmarks
• Ability to translate research questions into practical systems and collaborate across engineering and product teams
• Passion for open science, reproducibility, and community-driven research

Nice-to-Haves

• Skilled at public speaking, writing, and presenting research work to diverse audiences
• Actively participates in conferences, panels, and online forums to foster relationships and thought leadership
• Builds trust through transparent communication and consistent community engagement
• Serves as a go-to contact for external researchers, journalists, and partners