[Hiring] Staff Software Engineer @Confluent

Role Description

You'll help build Confluent Cloud's AI capabilities — the layer that lets customers bring AI and AI agents capabilities directly to their real-time data. Instead of moving data out to a separate system to run inference or build an agent, our customers do it in place, on streaming data, as part of the same platform they already use to move and process events at scale.

As an engineer, you'll own delivery of significant pieces of this product — not just writing code, but deciding how a capability should work across the services that make it up. The interesting problems here rarely live in one place:

• Shipping something like inference-on-streaming-data or an AI agent that reacts to live events touches several systems at once — the user-facing API, the services that manage model and agent lifecycle, the control plane that schedules and runs the work, and the serving layer that actually executes inference.
• You'll be expected to reason across those boundaries, make sound design calls, and get engineers inside and outside the team aligned on the approach.

What You Will Do:

• Design and build the backend services (primarily Go, Java, and Python) that run AI and model inference on real-time data.
• Own features end to end — drafting the design, aligning stakeholders inside and outside the team, and driving the decision to a conclusion.
• Make the technical calls on systems that span teams: model lifecycle, inference routing, and agent execution.
• Own the quality of what you ship — code, test coverage, documentation, operability, and rollout safety. This is production infrastructure serving live inference, so reliability isn't an afterthought.
• Make the engineers around you better through code review, design feedback, and being someone the team trusts with ambiguous, cross-cutting work.
• Participate in on-call for the services your team owns, and help keep the team's processes and rituals healthy.

Qualifications

• 10+ years of significant experience designing, building, and operating distributed systems or cloud-native backend infrastructure in production.
• Strong working knowledge of Kubernetes and distributed-systems patterns (control loops, API servers, high-scale control planes), plus the fundamentals — containerization, networking, resource isolation.
• Proficiency in at least one of Go, Java, or Python, and the willingness to work across all three.
• A track record of leading cross-team technical work: turning ambiguous requirements into designs others can rally behind.
• Excellent written and verbal communication — you can write a design doc that aligns people who don't report to you.

Requirements

• 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.

Benefits

• Belonging isn’t a perk here. It’s the baseline.
• We work across time zones and backgrounds, knowing the best ideas come from different perspectives.
• We make space for everyone to lead, grow, and challenge what’s possible.