Animaj is building proprietary, production-grade AI models (e.g. Sketch-to-Motion, Video-to-Motion) designed by and for filmmakers. These models are not experiments — they are the foundation of how we intend to produce animation at scale in the coming years.
The AI Tinkerer exists to amplify, extend, and operationalize this core technology.
Your role is to sit at the intersection of:
Animaj’s in-house AI models
The fast-moving external AI ecosystem
And the day-to-day reality of artists and production teams
Your mission is simple: turn Animaj’s proprietary AI stack into a true creative exoskeleton for artists — radically accelerating production while increasing creative control.
You'll join a tight-knit team of engineers, TDs, creative and production talent already pushing the boundaries of animation technology. You'll report to the SVP Production and be embedded within the Production department, with direct access to ongoing projects to test your findings in live conditions.
Extend Animaj’s Proprietary AI Models
Work hands-on with Animaj’s proprietary AI tools (e.g. Sketch-to-Motion, Video-to-Motion).
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Identify where these models can be:
- Combined with external tools
- Augmented with new inputs or workflows
- Better integrated into real production environments
Act as the bridge between research, engineering, and artists.
AI Tool Scouting & Evaluation
Scout and evaluate commercially available AI tools image, video, motion, audio, 3D).
Monitor and extract usable solutions from research papers and open-source projects.
Identify high-impact opportunities across the production pipeline — with a particular focus on animation and 3D asset creation.
Workflow Design & Prototyping
Design new production workflows that incorporate AI tools.
Prototype relentlessly inside real shows and real timelines.
Test and iterate on solutions weekly alongside existing production teams and artists.
Focus on time compression, scalability, and repeatability.
Knowledge Transfer & Internal Leverage
Collaborate with our engineering team to hand off promising solutions for customization and deeper integration.
Document learnings, workflows, failure modes, and articulate your research strategy and experimentation roadmap.
Present results to stakeholders across production and leadership.
