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Build and maintain low-latency, reliable backend APIs and data systems for Arena's evaluation products. Design schemas, scoring pipelines, usage metering, billing, auth/RBAC, multi-tenancy, and integrate research evals into durable, scalable product surfaces. Collaborate with infra, research, and product teams to ship enterprise-grade services and scale data pipelines.

Build and operate the core infrastructure for Arena's online evaluation systems: design low-latency, high-reliability APIs and gateways, implement enterprise-grade features (rate limiting, auth, metering, audit logging), instrument observability (tracing, latency, usage tracking), integrate with LLM providers and the evaluation platform, and collaborate with research and product teams to scale and harden systems for bursty, unpredictable traffic.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

The Senior Software Engineer will own feature development from design to deployment, focusing on user feedback and collaboration with ML researchers.

Lead design and scaling of People programs (onboarding, performance, compensation), advise leaders on org design and employee relations, build lightweight processes, manage compliance, and drive culture and headcount planning for a growing AI startup.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

The Product Solutions Engineer will integrate AI models with clients, deliver evaluations, improve processes, and collaborate on product development.

The Technical Recruiter will manage full-cycle recruiting, partner with hiring managers, and promote inclusive hiring practices while maintaining a strong candidate experience.

The Machine Learning Scientist will lead open-source research, design experiments, develop novel methodologies, and analyze large-scale data to advance AI model evaluation and transparency.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.