machine learning engineer for creative production

Grip provides infrastructure for enterprise creative production, helping global brands generate visual content at scale with control and consistency.

• Design focused, reliable LLM agents that translate brand intent into precise creative direction;
• Craft, test, and version prompts and build orchestration systems for agents and generation;
• Ground generation in brand rules, assets, and visual language with strict client isolation;
• Measure quality systematically using RAGAS or similar frameworks, A/B testing, and regression gates;
• Build pipelines that extract structure from documents, decks, scans, and imagery using OCR and vision models;
• Develop end-to-end automation pipelines for classification, extraction, proposal, and verification;
• Fine-tune LLMs and neural networks and manage models locally or through hosted infrastructure;
• Run pipelines against real client data and production volumes;
• Translate creative direction into structured AI workflows;
• Turn unstructured material into reliable data pipelines;
• Debug LLM and retrieval behavior across multiple pipeline layers;
• Communicate across engineering and creative teams.

• Strong production experience with LLM-based systems, including agents, RAG, and orchestration;
• Hands-on experience with evaluation frameworks and structured experimentation;
• Solid TypeScript and Python programming skills with experience shipping working systems;
• Experience training or fine-tuning models and managing their lifecycle;
• Practical experience with generative image pipelines;
• Ability to design systems with predictable, controllable, and production-ready AI behavior;
• Ability to work with constraints, data extraction, classification, and structuring;
• Nice to have: Experience with ComfyUI-based workflows, multi-tenant architecture, Elixir, C#.

• European Union;
• Grip provides production work with real client data and production volumes.