About the job
Roblox Build is our generative creation product, the platform where creators design, build, and publish 3D experiences. We are looking for a Senior Director of Generative AI to lead the Applied AI organization inside Build, responsible for turning state-of-the-art foundation models into high-quality, reliable creation systems at Roblox scale. This leader will own the full applied AI stack: model strategy and routing, model adaptation and fine-tuning, code generation (CodeGen), 3D layout generation (LayoutGen), and the evaluation science and infrastructure that tells us what actually works.
Responsibilities
Own model strategy and routing for Build, designing an intelligent model layer that selects the right model for each creation task based on quality, capability, latency, cost, and safety.
Lead model adaptation across the Applied AI org, including fine-tuning, distillation, synthetic data generation, human feedback pipelines, and preference optimization for Roblox-specific creation tasks.
Drive CodeGen capabilities forward by building AI systems that understand creator intent, reason across multi-file Roblox experiences, execute tools, and reliably make complex changes to existing games.
Build LayoutGen intelligence by developing the AI capability that turns a creator's intent into coherent 3D scenes, including object selection, spatial reasoning, placement, aesthetic quality, and iterative editing.
Own evaluation as a core technical discipline by building benchmarks, quality metrics, judge methodologies, experiment infrastructure, and human evaluation pipelines.
Close the feedback loop between model development, evaluation, and production, ensuring that quality signal flows continuously from creator outcomes back into training data, model updates, and release decisions.
Qualifications
Minimum
10+ years of experience in machine learning and AI, with 5+ years in senior technical leadership roles overseeing applied AI or ML engineering organizations that have shipped generative AI systems into production at scale.
Deep technical grounding across multiple areas of applied AI, including LLM post-training (RLHF, DPO, distillation), model routing and adaptation, agentic systems, code generation, or evaluation science.
Demonstrated experience building and leading high-performing organizations that span both AI research scientists and production engineers, including hiring, developing talent, and making difficult personnel decisions.
Strong evaluation fluency, with a track record of building eval frameworks that predict production outcomes, designing benchmarks that measure what matters, and building the infrastructure to run experiments continuously at model, prompt, and routing level.
Experience operating cross-functionally in complex technical organizations, with the ability to influence and align across research, product engineering, and platform teams without relying on hierarchy.
Preferred
No preferred qualifications listed.