AI Infrastructure Engineer
About The Position
PlayerZero is hiring an AI Infrastructure Engineer to build the infrastructure that supports agents working across the software development lifecycle. The AI Infrastructure team develops the runtime where agents execute, the context they can access, and the systems that coordinate work between agents and people. This infrastructure must be fast, composable, and able to scale. The role focuses on distributed systems and includes building durable, long-running agent workflows; designing how context from code, tickets, work items, and past decisions is represented and delivered; creating communication and escalation capabilities between agents and people; and developing sandbox infrastructure and fleet systems for agents to work across customer environments. Engineers are expected to own projects end to end, take prototypes into production, and balance reliability, scale, maintainability, and speed. In the first month, the engineer is expected to ship to production, learn how work moves through the platform, and take ownership of a component. Within three months, they should improve a meaningful part of the system measurably. Within six months to a year, they should be trusted to set the technical direction for a system layer and anticipate how it will behave as customers and workloads grow.
Requirements
- A builder mindset and experience owning projects end to end.
- Experience taking prototypes to production and understanding reliability and scalability tradeoffs.
- 3–5+ years building backend systems, ideally for real-time, stateful, or event-driven workloads.
- Strong distributed systems fundamentals, including message queues such as Kafka or Pulsar, distributed locks, pub/sub, and their failure modes.
- Experience building production infrastructure for LLMs, including areas such as tool calling, streaming, token budgets, or evaluation harnesses.
- Experience designing data models for messy, interconnected domains, including making schemas and storage choices that remain correct and scalable and considering query and serving needs.
- Ability to balance execution speed with code quality, unblock oneself, and involve the right people when needed.
Nice To Haves
- Hands-on experience with vector databases such as pgvector, Pinecone, or Turbopuffer, or with hybrid search systems.
- Experience with Kotlin/JVM, Rust, MongoDB, Postgres, Redis, WebSockets, or other real-time patterns.
- Experience building sandboxing or isolation systems with containers or microVMs such as Firecracker or gVisor, or managing fleets across heterogeneous machines.
- Experience with filesystem layers such as FUSE, overlay filesystems, or remote-backed storage.
- Experience with event sourcing, lineage, or provenance data.
Responsibilities
- Build agent infrastructure for long-running work, including sessions that survive failures, durable workflow state, lifecycle management, cancellation, and tracking work to completion across stages and participants.
- Design data models and systems for representing and delivering context between code, tickets, work items, and past decisions, including deciding what to precompute and what to resolve on demand.
- Build communication capabilities between agents and people, including messaging across agents and sessions, escalations, and interruption handling when new information arrives.
- Build sandbox infrastructure with environments that start quickly and can run at scale.
- Develop fleet infrastructure that lets agents work on machines where customer work already happens.
- Own projects end to end, taking prototypes to production while considering reliability, scalability, performance, data models, and maintainability.
- Ship production work, learn the platform end to end, and take ownership of a system component.
- Improve system capabilities measurably and, over time, set technical direction for an infrastructure layer.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
