Machine Learning Scientist - Open Source Lead

Machine Learning Scientist - Open Source Lead ABOUT ARENA INTELLIGENCE Arena Intelligence is the open platform for evaluating how AI models perform in the real world. Created by researchers from UC Berkeley’s SkyLab, our mission is to measure and advance the frontier of AI for real-world use. Millions of people use Arena Intelligence each month to explore how frontier systems perform — and we use our community’s feedback to build transparent, rigorous, and human-centered model evaluations. Leading enterprises and AI labs rely on our evaluations to understand real-world reliability, alignment, and impact. Our leaderboards are the gold standard for AI performance — trusted by leaders across the AI community and shaping the global conversation on model reliability and progress. We’re a team of researchers, engineers, academics, and builders from places like UC Berkeley, Google, Stanford, DeepMind, and Discord. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We’re building a company where thoughtful, curious people from all backgrounds can do their best work. Everyone on our team is a deep expert in their field — our office radiates excellence, energy, and focus. ABOUT THE ROLE LMArena is looking for a Machine Learning Scientist to lead our open-source research, including open data set and code releases, advancing how the world evaluates and understands AI models in the open. You’ll design, run, and share new methods and experiments that reveal what makes models useful, trustworthy, and capable, grounded in human preference signals and released openly for the full ecosystem and research community to build upon. In this role, you’ll be responsible for taking our commitment to openness from principle to practice, curating high-impact datasets, developing new methodology and reproducible benchmarks, and releasing code that enables the research ecosystem to push AI evaluations forward. Your work will shape the public leaderboard, power community tools, and strengthen transparency in AI evaluation worldwide. This role is deeply interdisciplinary, working with engineers, product teams, marketing, and the broader research community to advance how we compare models, analyze preference data, and understand factors like style, reasoning, and robustness. You’ll work closely with GTM teams as our spokesperson when it comes to outreach for our open research efforts: strengthening research partnerships, expanding research community participation, and championing programs that grow and support our research network. If you’re excited by open-ended questions, rigorous evaluation, and scientific communication and outreach, you’ll find a meaningful home here. We’re looking for: - 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 LMArena, engaging openly with the research community, and building a strong personal brand to help shape AI research culture. YOU’LL - 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 - Commun ... (truncated, view full listing at source)