Research Scientist – Controlled 3D Generation

<p><strong>Research Scientist – Controlled 3D Generation</strong></p> <p><strong>Location:</strong> Remote</p> <p><strong>About the Role</strong></p> <p>We’re seeking a Research Scientist passionate about <strong>3D generation, flow matching, and diffusion models</strong>. You’ll help advance the frontier of controllable 3D content creation—building models that generate consistent, editable, and physically grounded 3D assets and scenes.</p> <p><strong>What You’ll Do</strong></p> <ul> <li>Conduct cutting-edge research on <strong>flow-matching, diffusion, and score-based methods</strong> for 3D generation and reconstruction.</li> <li>Design and implement scalable training pipelines for controllable 3D generation (meshes, Gaussians, NeRFs, voxels, implicit fields).</li> <li>Develop techniques for <strong>conditioning and control</strong> (text, sketch, pose, camera, physics) and multi-view consistency.</li> <li>Analyse model behaviour through ablations, visualisations, and quantitative metrics.</li> <li>Collaborate with cross-disciplinary research, graphics, and infrastructure teams to translate research into production-ready systems.</li> <li>Publish results at top-tier venues and work with interns.</li> </ul> <p><strong>What You Bring</strong></p> <ul> <li>PhD (or equivalent experience) in Machine Learning, Computer Vision, or Computer Graphics.</li> <li>Published work on <strong>diffusion, flow-matching, or score-based generative models</strong> (2D or 3D).</li> <li>Strong engineering and problem-solving abilities: experience with <strong>PyTorch, JAX, or CUDA-level optimisation</strong>.</li> <li>Understanding of <strong>3D representations</strong> (meshes, Gaussians, signed-distance fields, volumetric grids, implicit networks).</li> <li>Solid grasp of <strong>geometry processing, multi-view consistency, and differentiable rendering</strong>.</li> <li>Ability to scale experiments efficiently and communicate complex results clearly.</li> </ul> <p><strong>Bonus / Pre