Arena

Machine Learning Engineer

• Architect and scale high-performance, real-time API and data systems
• Design and implement low-latency pipelines to process and analyze large-scale event streams
• Ensure reliability through robust data integrity, availability, and consistency mechanisms
• Mentor and guide engineers on infrastructure best practices, architecture, and performance tuning
• Collaborate cross-functionally with AI researchers, product leaders, and engineers to anticipate evolving infrastructure needs and deliver resilient, extensible systems

Senior Software Engineer, ML Infrastructure

Content Manager

• 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.

Scientific Content Lead

Social Media Manager

• Write, schedule, and manage content for X and LinkedIn that reflects our brand voice and values, tailored to each platform’s strengths and audience.
• Translate technical updates, product launches, and AI trends into engaging, accessible, and shareable posts.
• Maintain a forward-looking content calendar aligned with product milestones, community activity, and key events in the AI space.
• Monitor and participate in conversations across the AI and research ecosystem, contributing meaningfully and amplifying relevant voices.
• Drive growth in followers, reach, and engagement through experimentation, iteration, and real-time awareness.
• Build relationships with key community members, including AI researchers, developers, and early adopters.
• Develop platform-specific strategies to grow and sustain a presence on X, LinkedIn, and future channels.
• Track key performance metrics, analyze audience behavior, and use insights to inform content direction and tone.
• Collaborate with product to amplify launches, surface community activity, and bridge conversations across social and Discord.

Social Media Lead

Full-stack Engineer

• Design and build robust data pipelines to ingest, process, and transform user vote data to features essential for model performance evaluation.
• Collaborate with researchers and product leadership to understand product goals and necessary data.
• Design and implement solutions to generate result dashboards and reports, providing useful information for the public, model providers, and researchers.
• Ensure the integrity, data quality, and reliability of the pipelines.
• Scale our data infrastructure to accommodate increasing data volumes and evolving analytical needs.

Senior Software Engineer, Data Infrastructure

Technical Recruiter

• Partner with hiring managers to clarify role outcomes, success profiles, and interview plans; prioritize reqs against business goals.
• Run full-cycle recruiting: kickoff → JD refinement → sourcing → assessment → debriefs → offer → close.
• Build pipelines via targeted sourcing (passive outreach, talent communities, events, referrals, and thoughtful job posts).
• Maintain a structured, fair process: define competencies, calibrate rubrics, and uphold a consistent candidate experience.
• Hold a relentless drive for excellence in every stage—calibrating rubrics, tightening loops, and continuously improving the funnel.
• Track and improve funnel health (speed, quality, diversity signals); report weekly and quarterly progress with clear narrative and metrics.
• Drive offer strategy and negotiations aligned to leveling, compensation bands, and market insights.
• Nurture long-term networks of high-potential talent; keep a mapped bench for critical and future roles.
• Partner on employer brand moments (events, content, communities) that attract deep-tech talent.
• Coach hiring managers and interviewers on best practices and candidate excellence.

• Expand data structures and business logic to support new features
• Visualize feature performance and usage metrics through internal tooling and dashboards
• Own the full feature lifecycle—from technical scoping to deployment and iteration based on insights
• Identify and advocate for process improvements in development, testing, and deployment.
• Experiment with new tools and frameworks to enhance productivity and product capabilities

Software Engineer, Full Stack

Business Development Rep

• Own the Model Lab Relationships: Serve as the primary business lead for top global model labs, managing the relationship from research lead to procurement.
• Drive Revenue Expansion: Expand revenue across modalities (text, code, multimodal, vision, auto-eval) by aggressively aligning our eval programs with each lab’s training and research roadmaps.
• Strategic Enterprise Expansion: Identify and close select opportunities to bring LMArena’s eval capabilities to high-value enterprise accounts (e.g., Salesforce, IBM, ServiceNow) seeking to evaluate internal or vendor models.
• Secure Market Presence: Negotiate co-release and marketing rights for every major lab partnership to ensure LMArena retains mindshare as the industry standard.
• Lifecycle Management: Own contract renewals, upsells, and expansions, ensuring forward visibility into multi-quarter revenue commitments.
• Pipeline Hygiene: Maintain rigorous revenue forecasts that tie directly to delivery schedules, consumption pacing, and renewal cycles.
• Deal Architecture: Design and enforce pricing frameworks that balance predictability (committed contracts) with flexibility (usage-based consumption).
• Cross-Functional Alignment: Collaborate with Delivery and Product teams to ensure evaluation capacity, quality, and timing are aligned with customer expectations.
• New Product GTM: Ramp up on net new Eval or Data Products and build the strategy to sell them into existing and net new relationships.

Business Development Lead

Data Scientist

• Explore and analyze large, complex datasets to uncover patterns, biases, and causal relationships in model behavior and system performance.
• Formulate hypotheses about data quality, evaluation outcomes, and model performance — then design experiments to validate or refute them.
• Build reproducible analysis pipelines using Python, Pandas, NumPy, and Spark to process and interrogate large-scale data.
• Partner with ML researchers and engineers to design metrics and analyses that evaluate how models perform across domains, prompts, and tasks.
• Develop causal reasoning frameworks and statistical methods that help explain why models behave as they do — not just how well they perform.
• Communicate insights (for example, via blog posts) clearly to technical and non-technical partners, informing both research direction and infrastructure improvements.

General Counsel

• Come join our legal team to work on the most exciting commercial, intellectual property, and operational issues at the leading edge of AI.
• You will be the second member of the legal team and have an opportunity to advise across the business, but with a focus on enterprise sales, data licensing, IP transactions, and trademark and patent portfolio management.
• Drive Commercial Agreements: Lead the drafting, review, and negotiation of complex enterprise agreements, including Master Services Agreements (MSAs), Data Processing Agreements (DPAs), software licenses, vendor agreements, and strategic partnership contracts.
• Strategic IP Advice: Lead IP strategy for our product development and machine learning teams, including advising on open source and IP considerations with respect to AI model training.
• Build and scale our commercial legal processes, creating playbooks, standardizing templates, and implementing systems to support a high-growth sales organization. Build agents and internal software to support our operations, and procure cutting-edge legal tech tools that allow us to integrate home-grown and vendor-built software with ease.
• Support the management of Arena's worldwide IP portfolio including patents and trademarks; assist with patent prosecution, trademark registration, and enforcement.
• Provide counsel on machine learning, generative AI, and other technical areas, with the ability to engage effectively with technical teams on complex engineering and data evaluation issues.

Associate General Counsel, Commercial & IP

Analytics Engineer

• Own the design and implementation of analytics-ready data models, schemas, and tables in our data warehouse
• Build and maintain reliable data pipelines (batch and incremental) that transform raw event and vote data into standardized, trusted datasets
• Define and standardize core metrics used across product, research, and customer-facing evaluations
• Partner with product managers and researchers to translate evaluation questions into robust data models
• Develop and maintain dashboards, reports, and data artifacts used by internal teams and external partners
• Ensure data quality through testing, validation, monitoring, and documentation
• Orchestrate and schedule data workflows using Airflow or equivalent tools
• Optimize queries and pipelines to support large-scale analytical workloads
• Contribute to improving data discoverability, lineage, and documentation across the warehouse

Infrastructure Engineer

• Own technical strategy for LMArena’s core infrastructure - networking, APIs, services, data systems, and security foundations.
• Design and implement high-performance systems that scale to hundreds of millions of real-time interactions and evaluations per day.
• Collaborate with security engineering to embed anti-abuse measures, Sybil resistance, and trust systems into core platform architecture.
• Partner with product engineering to deliver infrastructure improvements that directly accelerate feature velocity and reliability.
• Build developer-facing primitives and tooling—internal APIs, SDKs, CI/CD enhancements—that make our platform more productive and safe to build on.
• Monitor, profile, and optimize system performance across backend services, databases, and networking layers.
• Drive operational excellence through observability, capacity planning, and incident response best practices.
• Mentor engineers across the company in building scalable, performant, and secure systems.

• Architect and build what will become our core modeling for data and evaluation products
• Own the full stack data, model training, and eval pipelines
• Help grow a culture of feedback and rapid product iteration as we build new features as a tight-knit team
• Conduct research into state-of-the-art evaluation methods and contribute to the long-term vision for a centralized, scalable evaluation platform.

• 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.

Machine Learning Scientist

• LMArena is looking for a Machine Learning Scientist to lead our open-source research.
• You'll design, run, and share new methods and experiments that reveal what makes models useful, trustworthy, and capable.
• You’ll be responsible for curating high-impact datasets, developing new methodology and reproducible benchmarks, and releasing code.
• Collaborate with engineers to implement and scale research findings into production systems.
• Analyze large-scale human voting and interaction data to uncover insights into model performance and user preferences.

Machine Learning Scientist – Open Source Lead

Security Engineer

• Own the product security vision for LMArena, ensuring security and trust are core to every stage of our product lifecycle.
• Design and implement platform-wide security features, including Sybil resistance, bot detection, reputation systems, and anti-abuse primitives.
• Lead threat modeling and security architecture reviews for new and existing product features.
• Collaborate with infrastructure and product engineering to design secure APIs, data flows, and identity systems that scale.
• Improve developer velocity by creating secure-by-default frameworks and tooling for internal teams.
• Partner with incident response to quickly assess, contain, and remediate security events, and lead deep postmortems to improve defenses.
• Stay ahead of the curve by monitoring emerging attack techniques and applying cutting-edge security research to our platform.
• Mentor engineers across the company on secure coding practices, architecture trade-offs, and operational security.