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Staff Software Engineer, Product

Arena
Bay Area, CA
Full Time

Job Description

About Arena Intelligence

Arena is the platform for evaluating how AI models perform in the real world. Founded by researchers from UC Berkeley's SkyLab, we're on a mission to measure and advance the frontier of AI for real-world use, and to build the foundation for everyone to understand, shape, and benefit from it.


Tens of millions of people use Arena each month to evaluate how frontier systems handle the work they actually do. The preferences they share power the most transparent, rigorous, and human-centered evaluations in AI. Leading AI labs, enterprises, and independent researchers rely on our work and open datasets to understand how models behave in real workflows: agentic coding, creative generation, professional productivity, and beyond. We go beyond leaderboards and decompose what human experience reveals about AI, so models advance toward the work people actually do.


We're a team of researchers, academics, builders, and creatives from UC Berkeley, Google, Stanford, and DeepMind. 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 together, in an office culture that radiates excellence, energy, and focus.


About the Role

We're looking for a Staff Software Engineer to own entire product areas at Arena — identifying what to build, designing and shipping the solution, driving it to measurable outcomes, and raising the bar for the team along the way. This is a hands-on IC role. You are the architect, not the delegator. You write code, design systems, and ship product — and you have a track record of doing this repeatedly with clear, attributable impact.


You’ll

  • Own product areas end-to-end — from identifying opportunities that aren't on anyone's roadmap, to building conviction, shipping, and driving to measurable outcomes

  • Design complete systems: data model, API design, frontend architecture (full-stack) or backend services and infrastructure (backend), and deployment strategy

  • Make high-stakes technical decisions under uncertainty — build-vs-buy, monolith-vs-service, when to prototype and when to harden — and be accountable for the outcomes

  • Work with ML researchers to turn research services into reliable, product-consumable systems

  • Ship and iterate — you don't hand off after v1. You measure, course-correct, and drive to sustained impact

  • Raise the technical and product quality bar on the team — introduce practices others adopt, unblock teammates, and create clarity out of ambiguity

  • Navigate cross-functional collaboration with product, design, research, and leadership to align on what matters

You’ll have

  • 8+ years of experience in software engineering, with a focus on product development

  • Deep experience building web applications spanning data model, API, frontend architecture, and deployment. You can design complete systems end-to-end and explain why you rejected alternatives at each decision point. You can explain how your architecture decisions shaped the product's capabilities and measurably improved product quality or team velocity.

  • A track record of repeated, attributable impact — multiple projects across different roles or companies where you can quantify outcomes (revenue, engagement, efficiency) and the impact sustained after you moved on

  • Personal technical depth, not delegation — you are the architect. You've dealt with real performance issues: slow queries, N+1 problems, caching, transaction isolation. You can go three levels deep on any decision and get more specific, not vaguer.

  • Product judgment and autonomous ownership — you've identified opportunities, validated them with evidence, built buy-in, shipped, and the bet paid off. You know when to prototype, harden, or kill work.

  • Clear, persuasive communication — you build buy-in across engineering, product, and leadership. You create clarity for others, not just yourself.

  • Genuine conviction about AI evaluation and Arena's mission — you can articulate why this domain matters and where the product should go

Bonus Points

  • Production experience with AI/LLM systems — inference pipelines, evaluation workflows, model integration, or AI-powered product features

  • Familiarity with our stack: NextJS, React, TypeScript, Tailwind, ShadCN, HonoJS, Postgres, Vitest

  • Experience with Supabase or Vercel's AI SDK

  • You've raised the bar on a team with measurable before/after — introduced practices, tools, or standards that others adopted

Our Tech Stack:

  • NextJS

  • React + TypeScript

  • Tailwind + ShadCN

  • HonoJS

  • Postgres

  • Vitest

What we offer

  • We offer competitive compensation and equity aligned to the markets where our team members are based. The base salary range will depend on the candidate’s permanent work location.

  • 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


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


Arena Intelligence provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.

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Job Details

Category
Software
Employment Type
Full Time
Location
Bay Area, CA (Hybrid)
Posted
Last updated
Jun 19, 2026, 11:35 AM

About Arena

Created by researchers from UC Berkeley, 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.

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