PlayerZero builds an engineering world model so SRE, QA, and support teams resolve incidents, prevent defects, and close tickets faster. Backed by Tech Square Ventures.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
- 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
More about this role
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...
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