RTB House Innovation & Technology Culture

RTB House has a highly technical, AI-first culture built around experimentation, engineering ownership, and solving large-scale performance problems. Deep learning is central to the company’s product rather than an add-on, and technical teams work on systems that process enormous volumes of real-time data while maintaining an emphasis on scalability, reliability, and code quality. 

• AI is at the core of the business: RTB House describes Deep Learning as its “superpower,” and its advertising engine is built around proprietary Deep Learning algorithms. The company has continued expanding that technology into areas such as contextual AI and large language models rather than relying solely on traditional machine-learning approaches.
• Engineers solve problems at significant scale: RTB House’s core Bidding Platform processes more than 20 million requests per second for real-time advertising auctions serving customers across 90+ countries. Engineering work involves challenges such as distributed systems, databases, Kubernetes, monitoring, low-latency infrastructure, scalability, and high availability.
• Technical quality is explicitly emphasized: Current engineers describe projects focused on “extreme performance and high code quality,” with clean code and code reviews treated as standard practices. Engineers are expected to test, deploy, monitor, and maintain what they build rather than handing responsibility off after development.
• Engineers are given meaningful ownership: Engineers can lead complex cross-team projects and own technical areas end to end. RTB House’s Platform Engineering model also creates self-service infrastructure so individual product teams can build and ship autonomously, while senior engineers can take responsibility for entire areas such as deployment, monitoring, or security.
• Teams are deliberately cross-functional: Engineering roles describe interdisciplinary, self-sufficient teams bringing together backend engineers, DevOps specialists, database experts, product designers, and other technical specialists. That structure gives engineers exposure to more of the system rather than confining them to a narrow piece of the technology stack.
• R&D continues beyond the company’s established products: After deploying Deep Learning throughout its algorithms, RTB House established dedicated AI Marketing Lab and Creatives Lab teams to develop new MarTech capabilities. More recently, it has embedded large language models into its Deep Learning stack; its systems have analyzed 1.5 billion URLs, matched 70 million offers, and generated 90,000 audiences using semantic intelligence.
• Employees describe technology as a source of excitement: One employee highlighted that “we develop game-changing technology,” while another said the company is constantly “adapting and creating new solutions” as technology evolves. They also emphasize smart teammates, collaboration, and being able to learn from colleagues with different perspectives.
• External signals:
  • Technical collaboration is a clear team strength: 94% of employees look forward to interacting with coworkers and 88% said company meetings are effective. (Comparably)
  • Employees are trusted with substantial responsibility: 98% of employees said people at RTB House are given a lot of responsibility, supporting the company’s emphasis on ownership and autonomy. (Great Place to Work)
  • Employees specifically recognize the technology and learning environment: Several external reviews rate RTB House a 5/5 and highlight its experienced AdTech team, training resources, and “interesting technology with a strong focus on performance and innovation.” (Glassdoor)

Bottom line: RTB House’s tech culture is built for people who want to work deeply with AI and large-scale engineering problems. Engineers operate systems handling more than 20 million requests per second, work in cross-functional teams, maintain high code-quality standards, and receive meaningful ownership over the systems they build.