Scientific Content Lead Job at Arena Intelligence, Inc. (Bay Area)

Job Description

Arena is seeking a Scientific Content Lead to define and defend the scientific credibility of the world’s most trusted AI evaluation platform. You’ll ensure that Arena’s methodology, data quality practices, and evaluation results are understood clearly by researchers, labs, policymakers, analysts, and enterprises. This role is deeply technical and highly cross-functional. You’ll work directly with our research team to translate evaluation science into rigorous public communication and content, anticipate methodological critiques, and uphold Arena’s commitment to transparency and neutrality.

Job Responsibility

Own Arena’s scientific communications strategy, ensuring that our evaluation methodology, benchmarks, and data quality practices are clearly understood and accurately represented externally
Lead Arena’s proactive data quality narrative, defending against common critiques and mischaracterizations through transparency, evidence, and high-integrity storytelling
Develop canonical explanations of Arena’s measurement approach, including Bradley-Terry-Luce-style ranking, confidence intervals, and uncertainty-aware interpretation
Ensure that Arena’s leaderboards are communicated responsibly: rankings are statistical estimates, small differences are often noise, and uncertainty must be preserved in public interpretation
Anticipate, track, and respond to methodological critiques, especially around contamination, overfitting, gaming, distribution shift, and evaluation validity
Partner closely with researchers to translate technical work into rigorous public materials, including methodology documentation, research posts, and open-source releases
Support Arena’s Academic Partnerships Program, strengthening scientific connectivity through collaborations, citations, and peer-reviewed credibility
Create briefing materials for high-stakes audiences, including frontier AI labs, policymakers, analysts, and enterprise partners, ensuring that technical nuance survives external scrutiny
Serve as a scientific editor and reviewer across external communications, stress-testing claims before they become public narratives

Requirements

8-10 years of experience in AI/ML, evaluation, research, or scientific communications, with deep familiarity in how frontier model performance is measured and debated
Strong technical background in machine learning, benchmarking, or model evaluation, with the credibility to engage directly with leading labs and researchers
Exceptional writing and communication skills, especially the ability to explain complex methodology clearly without oversimplifying or overstating conclusions
Track record of producing scientifically rigorous external-facing work, such as technical publications, evaluation reports, methodology documentation, or research translation
Deep comfort operating in ambiguity, where uncertainty, tradeoffs, and limitations must be communicated transparently rather than smoothed over
High editorial judgment and the ability to identify where scientific nuance is most likely to be misunderstood or weaponized
Collaborative mindset and experience partnering across research, product, policy, and communications teams

Nice to have

Direct experience working with large-scale human preference data, evaluation platforms, or benchmarking systems
Familiarity with common failure modes in AI evaluation, including contamination, overfitting, gaming, and distribution shift
Experience contributing to open source scientific tooling or methodology transparency efforts
Existing relationships within the AI research, safety, or evaluation community
Experience engaging with academic institutions, research alliances, or scientific journals
Comfort operating within neutrality and integrity constraints required of an independent evaluation platform

What we offer

Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs
The opportunity to work on cutting-edge AI with a small, mission-driven team
A culture that values transparency, trust, and community impact

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Scientific Content Lead

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