Senior Software Engineer - Streaming AI (Remote - Ontario / British Columbia)

Confluent, Inc
Mountain View, CA, United States
8 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
2 years minimum
Compensation
$144,200.0
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Artificial Intelligence Amazon Web Services Microsoft Azure C++ (Programming Language) Cloud Computing Distributed Systems Machine Learning Scala (Programming Language) Data Streaming Google Cloud Large Language Models
+9 more
Reliability of Systems Backend Core Data Information Technology Real Time Data Machine Learning Operations Stream Processing Confluent Golang

Job description

As a Software Engineer on the AI team, you will take ownership of the infrastructure that enables our ā€œin-place, at-scaleā€ 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).

Requirements

  • 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.

About the company

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.

One Confluent. One Team. One Data Streaming Platform.

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.

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