Computational Design Developer — Scripted Furniture Pipeline
Rozpočet: -
HOURLY / FULL_TIME
⭐ 4.65 (3)
AUS
python, 3d-rendering, 3d-design, reverse-engineering
We're an Australian custom furniture company that designs original pieces for hospitality venues. We generate photorealistic furniture concepts using AI image tools, and we manufacture at commercial scale — but right now there's a slow, manual gap between "approved concept image" and "drawings a factory can build from."
We want to close that gap with a fully scripted pipeline, and we're looking for someone to architect and build it with us.
The pipeline we want to build
Stage 1 — Multi-view renders → 3D mesh. Take a set of consistent rendered views of a furniture piece (front, side, three-quarter, etc.) and produce a clean 3D mesh. We're open on approach: AI image-to-3D services via API (Tripo, Meshy, Hunyuan3D, TRELLIS, or similar), photogrammetry adapted for synthetic imagery, or a hybrid. You should know the current landscape well enough to recommend what's production-viable today, not just what demos well.
Stage 2 — Mesh → manufacturable CAD. Raw AI meshes aren't buildable. This stage cleans, retopologises, and converts the mesh into a proper CAD representation — NURBS/BREP surfaces or a parametric reconstruction — with real-world dimensions, correct wall thicknesses, and geometry a factory can actually machine, cast, or fabricate. We currently work in Rhino 8, so Rhino.Compute / rhino3dm / Grasshopper experience is highly relevant, but we're open to your recommended stack if you can justify it.
Stage 3 — CAD → shop drawings. Scripted generation of dimensioned 2D shop drawings from the CAD file: orthographic views, sections, key details, exploded views where relevant, title blocks, and a basic bill of materials. Output as PDF + DXF/DWG. The goal is that a human reviews and annotates, rather than draws from scratch.
Stage 4 — Everything callable from the web. The whole pipeline needs to run headless, triggered by API calls, so it can sit behind an HTML-based product configurator. A user (or our team) adjusts a concept in the browser, the pipeline runs server-side, and manufacturable files come out the other end. You don't need to build the configurator front-end — but the pipeline must be architected as clean, documented services/endpoints that a front-end can plug into.
What you'll deliver
Architecture document — recommended tools, services, and data flow for the full pipeline, with honest notes on what's reliable now vs. what needs human-in-the-loop checkpoints.
Working proof of concept — one real furniture piece (we'll supply the renders) taken end-to-end: renders in, dimensioned shop drawing set + BOM out, via script.
Production pipeline — the PoC hardened into documented, repeatable code (Python preferred) with a defined API surface for the configurator integration.
Handover — clear documentation and a walkthrough session so our team can run, maintain, and extend it.
Required skills
Deep experience with mesh processing and repair (retopology, decimation, watertight checking) — e.g. Blender scripting, MeshLab, Open3D, PyMeshLab
Rhino 8 / Grasshopper / Rhino.Compute / rhino3dm, or an equivalent programmatic CAD stack (FreeCAD + Python, Fusion API, OpenCascade), with mesh-to-BREP conversion experience
Automated 2D drawing generation — scripted views, sections, dimensioning, and layout export to PDF/DXF
Strong Python; comfortable building headless services and REST APIs
Familiarity with current AI image-to-3D tools and their APIs, including their real-world limitations
Understanding of what makes geometry manufacturable (tolerances, wall thickness, joinery logic, material constraints) — furniture, joinery, product design, or industrial design background is a big plus.
How we'll work
We'll start with a paid discovery/architecture milestone before committing to the full build. We're a small, fast-moving company — you'll work directly with the founder, decisions get made quickly, and there's meaningful ongoing work if the pipeline succeeds (this is the manufacturing backbone of a larger product vision).
To apply, please answer
Describe a project where you converted mesh data into manufacturable CAD or drawings. What broke, and how did you fix it?
Which image-to-3D approach would you reach for first with synthetic renders (not photos) as input, and why?
What's your honest view on how much of Stage 2 (mesh → clean CAD) can be automated today vs. requiring a human checkpoint?
What's your preferred stack for Stage 3 (automated shop drawings), and can you share an example output?
Applications that answer these specifically will be prioritised over generic proposals.
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