The AI Experimentation Lab Manager will manage and maintain the lab's infrastructure at the AI X Lab — an experimentation-driven space dedicated to advancing our understanding of human–AI interaction, and an initiative of the Digital Data Design Institute at Nova SBE (an affiliate of the Harvard Business School AI Institute).
The role ensures the availability, scalability, and security of resources for experimentation with AI models, APIs, blockchain, and other emerging technologies. The Lab is equipped with cutting-edge technology for data collection and behavioral analysis, enabling researchers to design and study real-world experiments that explore how people engage with intelligent systems and how these interactions shape decision-making, collaboration, and learning.
Key responsibilities include:
- Manage and optimize the lab's computational infrastructure, including servers, cameras, sensors, and networks.
- Configure, monitor, and maintain AI model and API performance, ensuring efficiency and scalability.
- Automate processes and integrate systems using APIs and development frameworks.
- Leverage generative AI development tools to accelerate prototyping, internal tooling, and automation scripts, ensuring outputs are reviewed, tested, and production-ready.
- Ensure data and model security and integrity, implementing best practices in system administration and cybersecurity.
- Monitor and troubleshoot technical issues in hardware and software, including debugging AI models and analyzing execution logs.
- Support the team using open-source AI models and implementing experimentation and data analysis techniques.
- Manage video recording and streaming for experimentation, ensuring high-quality and synchronized digital content.
- Document processes, configurations, and best practices to ensure the replicability of experiments.
- A degree in Computer Science, Computer Engineering, or related fields.
- Experience managing and optimizing AI models, including deployment, versioning, and performance monitoring.
- Advanced knowledge of APIs for model and system integration and interoperability.
- Ability to develop and automate processes using Python, Bash, or other scripting languages.
- Proficiency in using generative AI coding tools (e.g., Claude, GitHub Copilot, Cursor) to accelerate software development, prototype new features, and support code review and debugging workflows.
- Experience in database management (SQL, NoSQL) and handling large datasets for experimentation.
- Experience with MLOps and DevOps, as well as using Azure and AWS cloud services, including virtual machine management, serverless computing, and data storage.
- Basic experience in server administration (Linux and Windows), local network (LAN) configuration, cloud computing management, and scalable infrastructure.
- Ability to manage data collection infrastructure, including streaming and multimedia recording for experimentation, using OBS Studio and NDI protocols.