Generative AI Engineer

About the job

We are looking for a Generative AI Engineer who can combine strong ML engineering with practical 3D generation knowledge. The ideal candidate should be comfortable working on diffusion-based generation systems, text-to-3D or image-to-3D workflows, asset validation pipelines, and scalable GPU inference systems. Key Responsibilities 1) AI Model Development • Research, evaluate, and implement state-of-the-art 3D generation models. • Build pipelines for text-to-3D asset generation. • Develop AI models capable of generating: • environments • structures • terrain • props • modular game assets • Improve quality, consistency, and usability of generated outputs for downstream game or simulation workflows. 2) Diffusion & Generative Systems • Build and optimize diffusion-based systems for: • 3D asset generation • mesh synthesis • texture generation • Evaluate, test, and integrate model ecosystems such as Tripo3D, Stable Diffusion 3D, Kaedim, and Scenario3D wherever relevant. • Fine-tune or adapt model pipelines for production-ready performance and better control over output quality. 3) Prompt-to-World Interpretation • Develop systems that convert natural language prompts into structured 3D scene specifications. • Design blueprint or schema-driven world definitions used to construct game environments. • Implement AI-driven layout reasoning for scene composition and greybox generation. • Bridge the gap between text prompts, generated assets, and final environment assembly. 4) Asset Validation & Pipeline Engineering • Validate generated assets for: • topology • mesh integrity • polygon count • game engine compatibility • Convert AI-generated assets into production-friendly formats such as GLB, FBX, and other engine-ready formats. • Build automated asset QA and optimization workflows to reduce unusable generations and improve production efficiency. 5) AI Infrastructure • Build scalable pipelines for: • GPU-based generation • batch asset generation • asset caching and reuse • Improve inference performance, resource usage, and pipeline scalability. • Collaborate with product, design, and engineering teams to integrate generation systems into a larger prompt-to-world platform.

Requirements

• Generative AI
• 3D Generation
• Python
• ML Frameworks
• Diffusion Models

Preferred Technologies

• Generative AI
• 3D Generation
• Python
• ML Frameworks
• Diffusion Models

About the company

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