AI Researcher - World Modeling

PlayerZero • San Francisco, CA

4d • Hybrid

About The Position

At PlayerZero, we are building the first Engineering World Model: a unified system that explains, predicts, and resolves production failures autonomously. Our mission is to move beyond the current landscape of "reactive tools" to create a new class of self-healing software where AI is an active participant in decision-making. By synthesizing codebases, tickets, and production telemetry, we've built a system that jointly represents intended behavior and runtime reality — and learns from where they diverge. That divergence is where production failures live. We're building the system that learns from every one of those moments. If that problem excites you, we should talk. This role is a hybrid position based out of our San Francisco office. All candidates must be located in the Bay Area to be considered. Relocation assistance is not available for this position. The AI team at PlayerZero is a lean, high-velocity group owning everything from agent architecture and model selection to evaluation infrastructure and reliability. We ship working systems over theoretical elegance, but we build with rigor — systematic evaluation, clear ownership, production-grade reliability. Your focus is the World Model , our core representation of how code behaves, what breaks in production, how failures relate to changes, and whose attention to prioritize. You'll design the abstractions that let our AI systems navigate the gap between intended and actual behavior, and get better at it with every incident they process. This means designing entity representations that link issues, code, PRs, traces, and people; building memory systems that accumulate institutional knowledge; and researching how to surface the right context at the right time.

Requirements

• MS or PhD in ML/AI, or 3+ years of research experience in knowledge representation, model training, or related areas
• Experience with fine-tuning, knowledge graphs, embedding models, and retrieval systems where relevance is a moving target
• Strong Python skills; able to move from a research problem to a working prototype without a lot of scaffolding
• Solid understanding of LLM-based systems and how context affects model behavior

Nice To Haves

• You've built knowledge graphs or entity-linking systems that had to survive noisy, real-world data
• You've worked with context window limitations and what context to include as a first-class optimization problem
• You've thought about how to represent code, changes, and issues in ways that enable learning
• You're drawn to systems that compound in value with every failure they process
• You’re interested in the intersection of code understanding, knowledge systems, and AI

Responsibilities

• Architect the Graph: Design entity representations linking code, PRs, telemetry, tickets, and developer behavior into a unified graph
• Build Institutional Memory: Develop memory and intent structures that accumulate resolution patterns to capture how your best engineers actually debug and decide, so that knowledge compounds across every future incident
• Improve Context Prioritization: Research and implement retrieval strategies that surface the right context for a given production task — what code, what history, what people, in what order
• Surface Implicit Signals: Prototype methods for capturing latent patterns (codebase navigation, issue clustering, resolution behavior, etc.) to sharpen our agents’ accuracy over time
• Ship It: Collaborate with Applied AI engineers to move research into production

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level