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Software Engineer, Data Acquisition

🏢 OpenAI · 📍 San Francisco · ⏰ FullTime
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Position Summary & Responsibilities

Overview:

The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Software Engineer to join our Data Acquisition team.

Responsibilities:

  • Own and lead engineering projects in the area of data acquisition including web crawling, data ingestion, and search.

  • Collaborate with other sub-teams, such as Data Processing, Architecture, and Scaling, to ensure smooth data flow and system operability.

  • Work closely with the legal team to handle any compliance or data privacy-related matters.

  • Develop and deploy highly scalable distributed systems capable of handling petabytes of data.

  • Architect and implement algorithms for data indexing and search capabilities.

  • Build and maintain backend services for data storage, including work with key-value databases and synchronization.

  • Deploy solutions in a Kubernetes Infrastructure-as-Code environment and perform routine system checks.

  • Conduct and analyze experiments on data to provide insights into system performance.


Qualifications:

  • BS/MS/PhD in Computer Science or a related field.

  • 4+ years of industry experience in software development.

  • Experience with large web crawlers a plus

  • Strong expertise in large stateful distributed systems and data processing.

  • Proficiency in Kubernetes, and Infrastructure-as-Code concepts.

  • Willingness and enthusiasm for trying new approaches and technologies.

  • Ability to handle multiple tasks and adapt to changing prioritie

🎯 Candidate Preparation Guide For OpenAI Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluating frontier research capability, hyper-ambitious yet safe AI scaling strategies, and extreme engineering velocity under high ambiguity.

📋 Preparation Priorities (Key Areas to Clarify):
  • 1. Incremental, bureaucratic, or overly cautious thinking that hinders fast iteration on frontier systems.
  • 2. Disregarding safety, alignment, or ethical implications of deploying advanced models.
  • 3. Inability to reason deeply about both theoretical ML concepts and massive-scale distributed systems infrastructure.
✨ Recommended Role Alignment Hook:

I thrive on building scalable systems that push the absolute boundaries of what frontier AI models can safely achieve.

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