About PlayerZero

PlayerZero is building a self‑healing system for software—automating defect detection, diagnosis, and remediation so developers ship with confidence. Teams use PlayerZero to spot issues before customers do, pinpoint root causes fast, and close the loop from incident to fix.

Our platform includes capabilities like Agentic Debugging and Code Simulations that let engineers reproduce complex scenarios, reason about failures, and validate fixes safely and quickly.

About the role:

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.

What You’ll Do

• 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.

You might thrive in this role if:

• 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).

Bonus Points