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Opportunities at SpAItial
Building the future of generative world models
We’re pioneering the next generation of World Models, shifting the paradigm from 2D pixels to AI-driven 3D simulations that inherently understand physics and geometry. Here’s how SpAItial is redefining domains like:
- Robotics
- AR/VR
- Gaming
- Cinema
Our bold mission is to transform how industries generate and interact with physically-grounded 3D environments.
About the Role
If you thrive in fast-paced innovation, love tackling uncharted challenges, and take pride in elevating the collective, this could be for you. We’re looking for collaborators whose passion aligns with ours:
"Be fearless in solving complexity. Pride in work. Prioritize team goals over personal ambitions."
Here, you’ll shape the trajectory of generative AI technology with a team that’s as ambitious as it is calls good work. Ready for impact? Let’s create history.
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Responsibilities
Driving vision to reality across
- World Models Architecture
- Designing generative world models capable of reasoning about space, time, and physics.
- Foundation Models Core Engineering
- Developing advanced image/video diffusion models.
- Distributed Infrastructure & Scale
- Training large-scale generative models on distributed cloud infrastructure.
- Prototyping & Storytelling
- Crafting demos and showcases for model prototypes in production-ready form.
- Data & Optimization
- Processing/ maintaining data pipelines.
- Productionizing and optimizing models at test-time.
Qualifications
We expect standout expertise and achievement in:
Education & Technical Depth
- PhD in Computer Science (or related field)
- Publications record in top-tier conferences (CVPR, ECCV/ICCV, NeurIPS, SIGGRAPH), with proven impact in generative models, vision, graphics, or physics-informed simulations.
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Domain Proficiency
✅ Generative Models (Diffusion, transformers, transformers for diffusion Annealing) ✅ Cutting-Edge Architectures (Diffusion Transformers, DiT, or analogous state-of-the-art advances) ✅ Deep Learning Frameworks (PyTorch or equivalent)
Practical Impact
- Demonstrate relentless ML experiment turnspeed using modern tools (e.g., LLMs for quick experimentation, modular code bases).
- Ability to balance experimentation with rapid iteration.
Commitment to Inclusion
SpAItial fosters a culture where diversity of thought, experience, and background enriches innovation. We’re proud to invite applications from all backgrounds, ensuring fairness throughout our recruitment process.
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Jessica, London
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