Research Scientist – Controlled 3D Generation
Skills & Technologies
Requirements
- Education
- PhD
Joblaze summary
In this role, the Research Scientist focuses on advancing controllable 3D content creation through innovative research in flow matching and diffusion models. Key skills include expertise in machine learning frameworks like PyTorch and JAX, along with a solid understanding of 3D representations and geometry processing. This position is ideal for candidates with a PhD or equivalent experience, particularly those with a strong publication record in generative models. The team at Stability AI emphasizes collaboration across disciplines to translate research into practical applications.
Joblaze insights
- Listed about a month ago on Joblaze — tracked directly from Stability AI's career page.
- Stability AI has 10 other open roles including Junior Software Engineer, Senior Site Reliability Engineer , Solutions Engineer.
- 270 active Machine Learning roles on Joblaze right now.
- 160 active PyTorch roles on Joblaze right now.
- 46 active JAX roles on Joblaze right now.
Quick facts
- Is the Research Scientist – Controlled 3D Generation role remote?
- Yes — Stability AI lists this as a fully remote position.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Flow Matching, JAX, Geometry Processing, CUDA, Machine Learning, Diffusion Models.
- What seniority level is this role?
- Stability AI targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Scientist – Controlled 3D Generation role at Stability AI.
From the original posting
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).
- 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.
Equal Employment Opportunity:
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.