Research Scientist – Controlled 3D Generation at Stability AI

**Who this is for** This role is for a research-oriented scientist with a strong background in geometry and generative AI, focused on pushing the boundaries of

Work type: remote

Location: Remote

Type: Full-time

Summary

**Who this is for** This role is for a research-oriented scientist with a strong background in geometry and generative AI, focused on pushing the boundaries of controllable 3D asset and scene generation. **Key highlights** You will advance the state-of-the-art in flow-matching and diffusion-based 3D generation, transforming research into scalable, production-ready systems alongside multidisciplinary teams. **You might be a good fit if you...** - Hold a PhD in Machine Learning, Computer Vision, or Computer Graphics. - Have a proven track record of published research in diffusion or score-based generative models. - Demonstrate strong engineering skills in PyTorch, JAX, or CUDA optimization. - Possess deep knowledge of 3D representations such as meshes, Gaussians, or implicit fields.

Job Description

Research Scientist – Controlled 3D GenerationLocation: 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.

• 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.

• 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).

• Knowledge of video-to-3D, image-to-scene, or 4D temporal generation.

• Background in physically-based rendering, simulation, or world-model architectures.

• Track record of impactful publications or open-source releases.

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

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