Machine Learning Scientist

Arena

Machine Learning Scientist evaluating AI models at Arena Intelligence. Designing experiments and metrics to understand model performance and reliability.

Posted 4/12/2026full-timeBay Area • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.
Competitive compensation and equity aligned to the markets where our team members are based.
The opportunity to work on cutting-edge AI with a small, mission-driven team.
A culture that values transparency, trust, and community impact.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

training large-scale modelsreward modelspreference modelsfine-tuning LLMsRLHFDPOcontrastive learningexperimental designstatistical frameworksdeep learning architectures

Soft Skills

collaborative mindsetiterative developmentcommunicationproblem-solvingpassion for open sciencetranslating research questionsworking closely with engineersaligning modeling goalscommunity-driven researchdesigning metrics

Certifications

PhD in Machine LearningPhD in Natural Language ProcessingPhD in Statistics