SkinBit réalise des scans corporels de la peau, analysés par des dermatologues. Le service aide les particuliers à suivre les changements cutanés et à repérer plus tôt les risques de cancer.
3D ENGINEER
Am I a fit — voir ma compatibilitéSkinBit is building the next generation of full-body dermatological scanners, combining high-resolution multi-camera imaging, controlled illumination, 3D reconstruction, and advanced algorithmic analysis to enable early skin cancer detection at scale. You will turn multi-camera captures into accurate, textured, clinically usable digital twins of a patient's…
Repères sur Skinbit
- Secteur
- Santé, pharmacie et biotech
- Modèle
- B2C
- Type d’entreprise
- Financée par du capital-risque
- Stade
- Startup early-stage
- Siège
- Paris
- Domaine officiel
- skinbit.com
- Création
- 2023 · 3 ans
- Effectif public
- 10 employés
- Financement total publié
- 6 M$Source ↗
- Dernière levée
- Seed · 2026-08-01
- Montant annoncé
- 6 000 000 $
- Offres ouvertes
- 1
Investisseurs mentionnés
Détails de l’offre
La description complète publiée par Skinbit.
Description de l’offre
SkinBit is building the next generation of full-body dermatological scanners, combining high-resolution multi-camera imaging, controlled illumination, 3D reconstruction, and advanced algorithmic analysis to enable early skin cancer detection at scale. You will turn multi-camera captures into accurate, textured, clinically usable digital twins of a patient's skin. A reconstruction pipeline already exists across point-cloud generation, mesh generation, and mesh texturing.
Your role
is to improve it on the axes that matter clinically and build the capabilities that do not exist yet. This is a hands-on senior execution role for someone who can bridge cutting-edge research and reliable production software.
- Improve geometric accuracy, surface completeness, texture fidelity, and reproducibility. Solve difficult full-body reconstruction cases including hair, fingers, ears, thin structures, and patient motion.
- Evaluate Gaussian Splatting and neighboring neural-rendering approaches against the current stack.
- Evaluate and integrate new depth-sensing hardware.
- Build pose-change, mesh-rigging, animation, and pose-dependent physics capabilities. Register additional photos captured in the wild onto the 3D model. Profile, optimize, scale, and integrate the reconstruction pipeline in production. 6+ years of professional experience in 3D reconstruction, geometry processing, SfM, multi-view stereo, photogrammetry, SLAM, or neural rendering. Engineering degree or MSc in Computer Graphics, Computer Vision, Computer Science, Applied Mathematics, or a related field. Expert-level Python and strong command of tools such as Open3D, PyTorch3D, COLMAP, trimesh, or equivalent. Strong foundations in multi-view geometry, camera calibration, registration, and mesh processing. Proven experience shipping 3D algorithms to production. Gaussian Splatting, NeRF, differentiable rendering, human-body reconstruction, or medical-imaging experience is strongly valued. High-impact work at the core of clinical performance. An interdisciplinary team spanning computer vision, hardware, and medicine. Mission-driven work supporting earlier cancer detection. A demanding, pragmatic, execution-focused deep-tech environment.