Engineering Manager, Data Acquisition (Foundations)

As the Engineering Manager for Data Acquisition at OpenAI, you will oversee a variety of critical responsibilities. These include leading and managing engineering projects related to web crawling and data ingestion, overseeing architecture and deployment of distributed systems, and collaborating with cross-functional teams to maintain seamless data flow. You’ll also play a pivotal role in mentoring team members and ensuring a high level of performance in all data acquisition initiatives.

To be considered for the Engineering Manager, Data Acquisition role at OpenAI, candidates should have at least 10 years of experience in software engineering—particularly with large-scale distributed systems and data processing. Additionally, a minimum of 4 years in managing engineering teams is crucial. Strong expertise in building web crawlers and working with Kubernetes is required, along with excellent communication skills and a passion for fostering innovation.

The role of Engineering Manager, Data Acquisition at OpenAI directly supports our mission to ensure that artificial intelligence benefits all of humanity. By leading efforts in data collection and management, you’ll be contributing to the foundation upon which our AI systems learn and evolve. This position is central to pushing the boundaries of AI technology in a responsible and innovative manner.

At OpenAI, the Data Acquisition team fosters a collaborative and inclusive culture. As an Engineering Manager, you will promote an environment of continuous learning and support your team through mentorship. We believe in empowering our engineers, valuing diverse perspectives, and driving a culture of innovation and improvement. Team members are encouraged to speak up and contribute their ideas, creating a vibrant ecosystem of creativity.

In this position, you will manage projects focused on enhancing web crawling services, developing efficient data ingestion systems, and optimizing existing data processes at OpenAI. You’ll work on projects that require collaboration with various teams, ensuring compliance with regulations while implementing best practices in software engineering to elevate the capabilities of our data acquisition strategies.

In answering this question, focus on specific projects or systems you have worked on where you managed large amounts of data or concurrent processes. Discuss the technologies you used, any challenges you faced—and how you overcame them—and outline your role in ensuring data reliability and performance across the systems.

When discussing your mentoring style, it's effective to share concrete examples of how you've implemented career development plans, provided feedback, and encouraged growth. Highlight the importance of open communication and creating opportunities for team members to engage in challenging projects that enhance their skills.

For this question, articulate your approach to fostering relationships. Discuss methods such as regular check-ins, clearly defined role responsibilities, and joint project kickoffs that ensure all stakeholders are aligned. Sharing an example of a successful cross-functional project can make your answer more impactful.

Discuss the best practices you value in engineering, such as test-driven development, continuous integration, and monitoring systems for performance. Explain how you have implemented these in past roles, and why they are critical to delivering high-quality software, especially in a fast-paced environment.

When answering this question, emphasize the importance of understanding both the technical and business objectives. You can discuss using frameworks for prioritization and how you keep communication channels open with leadership to ensure alignment and adapt to changing priorities.

Emphasize a specific challenge you faced, the steps you took to tackle it, and how your leadership contributed to a successful outcome. Discuss the impact of the solution and what you learned that could apply to future problems.

Detail your familiarity with Kubernetes, focusing on how you've used it to manage containerized applications. Discuss your experience with Infrastructure-as-Code tools, particularly any specific examples where it has improved deployment efficiency or reduced errors in past projects.

Discuss your understanding of data privacy regulations and detail your methods for ensuring compliance, such as conducting regular audits, providing training for team members, and collaborating closely with legal teams to stay updated on changes in regulations.

This is a chance to showcase your industry knowledge. Discuss challenges such as managing vast quantities of data, keeping up with technological advancements, and addressing data privacy concerns. Offer potential solutions or innovative ideas you believe could help tackle these challenges.

In your answer, express your passion for technology and innovation. Discuss the fulfillment you find in leading teams, solving complex problems, and contributing to projects that have a meaningful impact on advancing AI capabilities.