AI Infrastructure Engineer

Building great software takes more than writing code. It takes teams working together, and too often, the information they need is scattered across tools, conversations, and systems.

PlayerZero is an AI platform that connects work across enterprise Support, Engineering, Product, QA, and SRE teams, helping them resolve issues faster, improve software quality, and plan what to build next.

At its core is our Engineering World Model: a living understanding of a company’s software, how it runs, and the people and decisions behind it. By connecting codebases, tickets, production telemetry, and the conversations that shape the work, our model links what software is intended to do with how it actually behaves in production—and learns from where the two diverge.

Our agents (we call them Players :) use that context to take on work across the software lifecycle. They learn from each outcome, earning trust and taking on more responsibility over time.

We’re building a future where AI plays an active role in how teams plan, build, and run software. We’re looking for people across disciplines to help shape it.

Most AI tools help one person perform a task faster, a session at a time. But real software work doesn’t happen in one session or one tool. A bug moves from a customer to a ticket to a Slack thread to a PR to a deploy, across people and systems that never see the whole picture. The most interesting context lives between those systems of record, and so does the real work.

For our agents to take on that work, something has to sit in between. The AI Infrastructure team builds it: where agents execute, what context they access, and how work moves between agents and people. Because the work runs through this infrastructure, it’s also what lets the Engineering World Model learn from every outcome and earn our agents the right to do more. It has to be fast, composable, and built to scale.

We’re hiring infrastructure engineers who like hard distributed-systems problems and want to own them end to end.

• Build agentic infrastructure. Our agents don’t answer a question and disappear. They take on units of work that run for hours or days, change hands, and keep driving toward completion. You’ll build the runtime that creates continuity for that work: long-running sessions that survive failures, durable workflow state, lifecycle and cancellation, and workflows that track each piece of work across every stage and every party involved.

• Architect how context is represented and delivered. Because work flows through our platform, we’re present when the connections between code, tickets, units of work, and past decisions are made. The context between these systems of record is necessary for agents to do their work well, and the data models behind it shape everything an agent can know. You’ll design how that context is represented and delivered: what gets precomputed ahead of time versus resolved on demand, and how the right slice reaches each agent at the right moment.

• Build the communication layer. Real work doesn’t happen in isolation. Agents work alongside other agents (subagents, teammates, agents on customer machines, agents over MCP) and alongside people. You’ll build the layer that connects them: messaging between agents within and across sessions, escalations that pull in the right person at the right time, and agents that can be interrupted when new information arrives.

• Scale sandboxes and fleets. An organization’s work is spread across laptops, QA fleets, production hosts, and ephemeral scripts. You’ll build our own sandbox infrastructure, environments that spin up instantly and run by the hundreds, and the fleet layer that lets agents work on the machines where customer work already happens.

You don’t need experience in all of these. We expect you to go deep on one or two and pick up the rest along the way.

• A builder mindset. You own projects end to end, think carefully about data models, performance, and maintainability, and have taken prototypes to production with a clear sense of the reliability and scale tradeoffs.

• 3–5+ years building backend systems, ideally real-time, stateful, or event-driven workloads, with strong distributed systems fundamentals: message queues (Pulsar, Kafka), distributed locks, pub/sub, and their failure modes.

• Experience building infrastructure for LLMs in production: tool calling, streaming, token budgets, or evaluation harnesses, and a sense of what agents need from the systems beneath them.

• Experience designing data models for messy, interconnected domains. You know how to structure data so it stays correct as it grows and changes, and you think about how it will be queried and served before you write the schema. You’ve made schema and storage decisions that held up under scale, and you know the cost of getting them wrong.

• You keep work moving. You balance speed of execution with code quality, unblock yourself when you can, and pull in the right people at the right time when you can’t.

Bonus Points

• Hands-on with vector databases (pgvector, Pinecone, Turbopuffer) or hybrid search systems

• Experience with Kotlin/JVM (our primary stack) or Rust, MongoDB, Turbopuffer, or Redis, and WebSockets or other real-time patterns

• You’ve built sandboxing or isolation systems with containers or microVMs (e.g., Firecracker, gVisor), or managed fleets across heterogeneous machines

• You’ve worked on filesystem layers such as FUSE, overlay filesystems, or remote-backed storage

• You’ve worked with event sourcing, lineage, or provenance data, where the record of what happened is itself a product

In your first month, you’ve shipped to production, learned how work moves through the platform end to end, and taken ownership of a real component in one of the layers above.

By three months, you own a meaningful piece of the system and have made it measurably better: longer-running work that survives failures, faster sandbox startup, context that reaches agents more reliably, or fewer dropped handoffs between agents and people.

By six months to a year, you’re the person the team trusts with a layer of the machine. You set its technical direction, anticipate where it will break as customers and workloads grow, and your work shows up directly in how much our agents can take on.

PlayerZero is an early leader in AI-powered production engineering, defining how enterprises use AI to understand, improve, and operate their software.

We’re a small team with meaningful problems to solve and room for each person to make their mark. Whether you’re building the technology, working with customers, or growing the business, you’ll help shape the product, the company, and what customers expect from this emerging category.

If you’re excited by work where the answers aren’t all written yet, and want a hand in writing them, you’ll fit right in.