Research Scientist – Controlled 3D Generation

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Work mode

Remote From the posting source

In-office days

0 days From the posting source

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About this role

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

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