Senior AI Engineer

Grip is shifting enterprise content production from manual, tool‑driven workflows to programmable systems that generate high‑quality visual output  a Senior AI Engineer, you build the control layer behind that shift: AI Controllers, reusable templates, and workflow primitives that translate brand intent into deterministic, production‑ready outputs.

This is not prompt engineering. This is designing systems that constrain, guide, and scale generative models into reliable creative production pipelines used by global brands.

• Architect and ship AI Controllers that encode visual logic (composition, layout, constraints) into reusable systems
• Translate creative direction into structured pipelines (e.g., turning logo safe zones into enforceable spatial constraints)
• Design templates that produce consistent outputs across thousands of variations

• Build and optimize Comfy
  
  UI pipelines integrating 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

• Work hands‑on with diffusion models, encoders, and latent space manipulation
• Define how prompts, conditioning, and control signals interact across the pipeline
• Ensure outputs meet brand and visual standards—not just “good images,” but usable assets

• Contribute to new node definitions and AI capabilities within Grip’s platform
• Specify technical requirements for new features across Python/PyTorch systems
• Collaborate with engineers and creative teams to push what’s possible in automated content generation

You build systems that make AI predictable, controllable, and production‑ready—especially in visual domains.

• Strong experience with generative AI systems (diffusion models, conditioning, latent space control)
• Hands‑on work in Comfy
  
  UI 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 thinking—actual usage)

• In systems, not prompts—how components interact, fail, and scale
• In constraints—how to guide models toward predictable outputs
• In outputs—what makes something production‑ready vs. visually interesting
• Pragmatically—you ship working solutions and refine based on results

• Translating abstract creative direction into structured AI workflows
• Debugging generation pipelines across multiple control layers
• Balancing flexibility with control in template systems
• Communicating across engineering and creative teams

• The Platform Position. Grip is infrastructure for enterprise creative production—powering global brands to generate content at scale with control and consistency.
• The Market Timing. AI‑generated content is moving from experimentation to production. The challenge is no longer generation—it’s control, reliability, and scale.
• The Technical Moat. You’ll work with Comfy
  
  UI‑based workflows, multi‑tenant architecture, and a stack spanning Type Script, Python, Elixir, and C#—built for production, not demos.
• The Work. You will define how AI is actually used in real‑world creative systems—turning unpredictable models into dependable pipelines.