AI Infrastructure Engineer

📋 Job Overview

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. 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.

🏢 About PlayerZero

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.

🎯 The Role

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.

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• 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

🎁 Benefits

Compensation: $150K – $210K • Offers Equity. PlayerZero determines compensation using market benchmarks, role scope, and candidate experience. Final offers are based on experience, expected impact, and internal equity, and typically include base salary, equity participation, and benefits.

🛂 Visa & Eligibility

No visa or eligibility information provided.