Unknown Company

Technical Director - AI Roblox Studio

san mateo, ca • Posted 1 weeks ago
Hybrid Full Time IT & Technology

Hands-on individual-contributor role owning the technical direction of the agentic AI systems embedded in Roblox Studio as it evolves into an AI-Native IDE where intelligent systems plan, act, validate, and iterate alongside creators.

$399,420 – $457,970 / yr

Location

San Mateo, CA

Hybrid

Seniority

Director

Compared with 41 other Engineering roles: Common across similar roles Unique to this role

What you'll do

Responsibilities

  • Design and build the agentic AI systems at the core of the AI-Native IDE (planning, execution, validation, evaluation) so they are production-grade and reliable Common
  • Decide how Studio uses coding models, agents, retrieval, tool calling, and context management across the Assistant and the broader creator workflow Common
  • Own the quality bar and evaluation strategy, standing up quantitative and qualitative eval pipelines including human-in-the-loop
  • Optimize systems for latency, throughput, and cost (caching, routing, distillation, quantization)
  • Bring reusable patterns and best practices to teams across Studio, raising applied-AI fluency of Studio engineering
What you'll need

Experience

  • 12+ years of engineering experience, including ownership of large-scale, long-lived platforms
  • Deep, hands-on experience building LLM-powered and agentic systems in production, beyond chat-based interfaces Common
  • Strong systems and software engineering skills — able to design the architecture and write the code
  • Track record defining evaluation frameworks for AI product quality, combining automated metrics with human review, and optimizing inference for latency, throughput, and cost
  • Strong background in developer tools or IDE-like platforms; proven ability to set technical direction across multiple teams as an individual contributor
  • Preferred: background in coding models or code-generation systems, and/or research experience in reinforcement learning (RLHF, RLAIF, preference modeling)
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