About the Team:

We're a small, focused engineering team building the AI capabilities of Confluent Cloud. Our job is to make it possible to run machine learning and AI agents directly on real-time data - without customers having to stitch together a separate stack to do it. We own our products end to end, from the user-facing API down to the serving layer that runs inference in production, and we work closely with the broader platform teams whose systems we build on top of. It's a high-ownership, high-autonomy environment: small enough that what you build ships and matters, broad enough that the problems are genuinely hard.

About the Role:

As a Software Engineer on the AI team, you will take ownership of the infrastructure that enables our "in-place, at-scale" AI value proposition. You aren't just building a feature; you are architecting the systems that allow customers to run complex inference and AI agents directly on streaming data, eliminating the need to move data to external stacks. This is a high-impact role where your work on scalable, cost-efficient serving layers directly drives Confluent's growth and redefines the possibilities of real-time data.

We are looking for engineers who thrive on the technical complexity of large-scale distributed systems. You will tackle deep infrastructure challenges across networking, compute, and security to ensure our AI capabilities are as reliable as the core data plane itself. If you are passionate about building the foundational systems that power the next generation of AI in the cloud, this is the place to do it.

What You Will Do:

• Design, develop, and operate large-scale, high-performance infrastructure that powers Confluent Cloud.
• Build foundational software to improve reliability, scalability, and efficiency across cloud environments.
• Work on distributed systems challenges such as consensus algorithms, failover strategies, and resource allocation.
• Collaborate with teams across Confluent to optimize and enhance infrastructure for real-time data streaming use cases.
• Troubleshoot and improve system reliability, observability, and performance across multiple cloud providers (AWS, Azure, GCP).

What You Will Bring:

• 2-5 years of industry experience designing, building, and supporting backend systems in production.
• Strong fundamentals in distributed systems, cloud infrastructure, and networking.
• Experience in building and operating large-scale, high-availability systems.
• Good understanding of cloud platforms (AWS, Azure, or GCP) and their services.
• Proficiency in Java, Scala, C++, Go, or other statically typed languages.
• A self-starter with strong problem-solving skills and the ability to work in a fast-paced environment.
• BS, MS, or PhD in computer science or a related field, or equivalent work experience.

What Gives You an Edge:

• Exposure to model serving, LLM/agent infrastructure, or streaming data systems.
• Note - You don't need a background in ML research or model training - this role is about building and operating the platform that serves AI reliably at scale, not inventing the models.

Ready to build what's next? Let's get in motion.