AI Infrastructure Software Engineer
About The Position
PlayerZero is building a self-healing system for software that automates defect resolution and development. We are used by engineering and support teams to: autonomously debug problems in the software (technical support) fix issues directly in the code prevent these problems from recurring PlayerZero is backed by leading investors such as Foundation Capital, WndrCo, and Green Bay Ventures — and operators like Matei Zaharia, Drew Houston, Dylan Field, Guillermo Rauch, among others. We believe that as software development speeds up, engineering and support teams face greater challenges maintaining software for their customers. We see this as an opportunity to reinvent how software is supported. We’re looking for an experienced backend / infrastructure engineer who loves turning research prototypes into rock-solid production systems. You’ll design and scale the core services that power our AI inference stack—from data ingestion and feature stores to retrieval pipelines and GPU orchestration. If you’re obsessed with performance, correctness, and shipping fast, you’ll feel at home here.
Requirements
- 2–5+ years of experience building scalable backend or infrastructure systems in a production setting.
- A builder mindset — you like owning projects end-to-end and are thoughtful about data models, performance, and long-term maintainability.
- Experience transitioning prototypes to production with an understanding of tradeoffs in reliability and scale.
- Comfort with data engineering workflows — parsing, transforming, indexing, and querying structured or unstructured data.
- Exposure to search infrastructure or LLM-backed systems (e.g. document retrieval, semantic search, evaluation, or prompt engineering).
Nice To Haves
- Hands-on with vector databases (e.g., pgvector, Pinecone, Weaviate) or inverted-index search (Elasticsearch, Lucene).
- Experience operating GPU clusters (Kubernetes, Ray, KServe) or tuning model-parallel inference.
- Familiarity with Go / Rust (our primary stack) and TypeScript for the occasional full-stack tweak.
- Deep knowledge of observability (OpenTelemetry, Grafana, Datadog) and performance profiling.
- Contributions to open-source ML or infrastructure projects.
Responsibilities
- Own critical services end-to-end—from architecture and design reviews through deployment, observability, and SLOs.
- Scale LLM-driven workloads: build retrieval-augmented generation pipelines, vector indexes, and evaluation harnesses that handle billions of events per day.
- Design data-intensive systems: streaming ETL, columnar storage, and time-series analytics that feed our self-healing algorithms.
- Optimize for cost & latency across CPUs, GPUs, and serverless runtimes; profile hot paths and squeeze every millisecond.
- Champion reliability: automate testing, chaos drills, and progressive delivery so new models roll out safely.
- Collaborate cross-functionally with ML researchers, product engineers, and customers to ship features that matter.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
