Machine Learning Scientist - Open Source Lead

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.

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

Who is LMArena?

Created by researchers from UC Berkeley’s SkyLab, LMArena is an open platform where everyone can easily access, explore and interact with the world’s leading AI models. By comparing them side by side and casting votes for the better response, the community helps shape a public leaderboard, making AI progress more transparent, and grounded in real-world usage.

Why Join Us?

Trusted by organizations like Google, OpenAI, Meta, xAI, and more, LMArena is rapidly becoming essential infrastructure for transparent, human-centered AI evaluation at scale. With over one million monthly users and growing developer adoption, our impact is helping guide the next generation of safe, aligned AI systems—grounded in open access and collective feedback.

Our work is regularly referenced by industry leaders pushing the frontier of safe and reliable AI. Sundar Pichai, Jeff Dean, Elon Musk, and Sam Altman.

• High Impact: Your work will be used daily by the world’s most advanced AI labs.
• Global Reach: Develop data infrastructure powering millions of real-world evaluations, influencing AI reliability across industries at the top-tier
• Exceptional Team: We are a small team of top talent from Google, DeepMind, Discord, Vercel, UC Berkeley, and Stanford.

Requirements:

• PhD or equivalent research experience in Machine Learning, Natural Language Processing, Statistics, or a related field
• Uses personal and professional platforms to amplify open research initiatives and invite collaboration.
• 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

What we offer:

• The cash compensation for this position has not yet been finalized. Actual compensation will depend on job-related knowledge, skills, experience, and candidate location.
• Competitive salary and meaningful equity
• Comprehensive healthcare coverage (medical, dental, vision)
• The opportunity to work on cutting-edge AI with a small, mission-driven team
• A culture that values transparency, trust, and community impact

Come help build the space where anyone can explore and help shape the future of AI.

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