As a Senior AI Engineer at Grip, you build the control layer behind the shift from manual, tool-driven workflows to programmable systems that generate visual output at scale.
## What you'll do
Build AI-Controlled Content Systems
- Architect and ship AI Controllers that encode visual logic (composition, layout, constraints) into reusable systems
- Translate creative guidelines into structured pipelines (e.g., converting logo safe zones into enforceable spatial constraints)
- Design templates that generate consistent outputs across thousands of variations
Develop and Scale AI Workflows
- Build and optimize ComfyUI pipelines with multiple models, refiners and control mechanisms
- Combine segmentation, depth maps and latent constraints to guide generation toward production-grade outputs
- Iterate on workflows based on output quality, failure modes and client requirements
Own Model Behavior and Output Quality
- Work hands-on with diffusion models, encoders and latent space manipulation
- Define how prompts, conditioning and control signals interact in the pipeline
- Ensure outputs meet brand and visual standards—not just beautiful images, but usable assets
Extend Platform Capabilities
- Contribute to new node definitions and AI capabilities in Grip's platform
- Specify technical requirements for new features in Python/PyTorch systems
- Collaborate with engineers and creative teams to expand capabilities in automated content generation
## Who you are
You build systems that make AI predictable, controllable and production-ready, especially in visual domains.
You have:
- Strong experience with generative AI systems (diffusion models, conditioning, latent space control)
- Hands-on work in ComfyUI or similar node-based pipelines
- Solid programming skills (Python, PyTorch) with experience shipping working systems
- Experience training or fine-tuning models and managing their lifecycle
- Exposure to visual tools like Photoshop, Blender or similar (not optional—actual hands-on use)
You think:
- In systems, not prompts—how components interact, fail and scale
- In constraints—how to steer models toward predictable outputs
- In outputs—what makes something production-ready versus visually interesting
- Pragmatically—you ship working solutions and refine based on results
Your skills include:
- Translating abstract creative guidelines into structured AI workflows
- Debugging generation pipelines across multiple control layers
- Balancing flexibility and control in template systems
- Communication between engineering and creative teams