Senior Software Engineer - Stream Processing

Introduction

At IBM Software, we transform client challenges into solutions. Building the world’s leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You’ll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation. With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM’s product and technology landscape. Here, you’ll have the tools and opportunities to advance your career while creating software that changes the world. With Confluent, data doesn’t sit still. We put information in motion, streaming in near real time so organizations can react faster, build smarter, and deliver experiences as dynamic as the world around them.

We’re not just building better tech. We’re rewriting how data moves and what the world can do with it. With Confluent, data doesn’t sit still. Our platform puts information in motion, streaming in near real-time so companies can react faster, build smarter, and deliver experiences as dynamic as the world around them.

It takes a certain kind of person to join this team. Those who ask hard questions, give honest feedback, and show up for each other. No egos, no solo acts. Just smart, curious humans pushing toward something bigger, together.

Your Role And Responsibilities

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'll 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).

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 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.

Preferred Technical And Professional Experience

• Exposure to model serving, LLM/agent infrastructure, or streaming data systems.

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.