Arena Intelligence
Software Engineer - Data Infrastructure
About this role
Arena Intelligence seeks a Data Infrastructure Software Engineer to design and build data pipelines processing millions of user votes for AI model evaluation. You'll collaborate with researchers and product teams to ensure data quality, generate insights dashboards, and scale infrastructure to support rigorous AI evaluation and human preference analysis.
What you'll do
- Design and build robust data pipelines to ingest, process, and transform user vote data into evaluation features
- Collaborate with researchers and product leadership to understand data requirements and business goals
- Create dashboards and reports for public, model providers, and research audiences
- Ensure data integrity, quality, and reliability across production pipelines
- Scale data infrastructure to handle growing data volumes and analytical demands
- Optimize and debug production data pipelines for performance
What they're looking for
- Data engineering and big data technologies
- SQL
- Python (preferred) or Scala/R
- Apache Spark or Ray Data
- Databricks or Snowflake
- Data pipeline design and optimization
- Delta Lake or streaming tables (bonus)
- Machine learning exposure (bonus)
Benefits
- Competitive base salary and equity
- Medical, dental, and vision coverage
- Work on cutting-edge AI evaluation technology
- Small, mission-driven team environment
- Culture emphasizing transparency and trust
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Arena Intelligence
Arena Intelligence builds a platform for evaluating AI model performance through real-world testing and human preference data, powering insights for AI labs and enterprises. The company is hiring data engineers, security engineers, machine learning scientists, and customer-facing technical roles to scale its evaluation infrastructure, protect against misuse, and deliver custom solutions to customers.
View all jobs at Arena IntelligenceLikely interview questions
- Describe a complex data pipeline you designed—what challenges did you face and how did you optimize it?
- How have you approached ensuring data quality and integrity in high-volume production systems?