Apply on Employer Site

LMArena · 4 weeks ago

Machine Learning Scientist

San Francisco Bay Area

Full-time

Onsite

Senior Level

LMArena is an open platform for evaluating AI models, created by researchers from UC Berkeley’s SkyLab. The Machine Learning Scientist will design and analyze experiments to uncover insights into AI model performance and contribute to the scientific foundations of understanding AI at scale.

Artificial Intelligence (AI)Information ServicesMachine LearningProduct Research

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

Contribute to the scientific integrity and transparency of the LMArena leaderboard and tools

Qualification

Large-scale model trainingMachine LearningStatistical analysisPythonML research librariesNatural Language ProcessingExperiment designDeep learning architecturesOpen scienceCommunity-driven researchCollaboration

Required

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 modeling goals with user needs

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