The work
You will create stylized 3D models and digital assets from creative briefs and references. The work focuses on clear visual variations, production-ready optimization, and material-driven design.
You will document your workflow, apply feedback to revised assets, and work across low-poly, voxel, handcrafted, and other stylized approaches.
- Create stylized 3D models and digital assets from briefs and visual references.
- Recreate materials such as clay, fabric, paper, and textiles.
- Use topology, UV mapping, lighting, shading, rendering, and material-creation techniques.
- Produce consistent asset variations and optimize them for production use.
- Document workflows and revise assets based on feedback.
What it pays and takes
Compensation is paid in USD, with earning potential based on approved tasks. This is a remote project open worldwide, and English is required for the work.
- Compensation: Paid in USD based on approved tasks.
- Project length: 8 to 10 weeks.
- Time: 20+ hours per week, with up to 40 hours available.
- Part-time option: Artists working up to 20 hours per week are welcome.
- Location: Remote and open worldwide.
- Language: Fluent English.
- Portfolio: A strong portfolio showing stylized 3D work and material exploration is required.
- Tools: Capability in one or more of Blender, ZBrush, Adobe Creative Suite, After Effects, 3ds Max, or Cinema 4D.
- Helpful skills: Relevant plugins and scripts, non-photorealistic rendering, visual storytelling, and asset optimization.
- Work style: Strong communication, attention to detail, and time management.
- Experience: Entry-level artists, emerging digital artists, and experienced professionals are welcome.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind artificial intelligence, including preparing images and other digital examples that help models learn. People with strong creative or technical skills are paid to produce and review this work because their judgment helps create more useful training data.