SpAItial
Research Scientist - 3D Diffusion(m/w/x)
Vollzeitnur vor OrtKeine Angabe
München
Neuer Job?Nejo!
Dein persönlicher KI-Karriere-Agent
SPSpAItial
Research Engineer(m/w/x)
München
VollzeitVor OrtBerufserfahren
AI/ML
Data Science
Nejo KI-Zusammenfassung
Designing generative 3D ML methods for physically-grounded 3D environment simulation. Expertise in diffusion models and transformers required. Equal opportunity employer.
Anforderungen
- Bachelor's or Master's degree, or equivalent project/research experience
- Degree in computer science, machine learning, computer vision, graphics, robotics, or related field
- Strong fundamentals in deep learning
- Strong fundamentals in generative models
- Expertise in diffusion models and transformers
- Solid understanding of 3D processing concepts
- Knowledge of camera geometry, depth, reconstruction, point clouds, meshes, or Gaussian splats
- Proficiency in Python
- Proficiency in deep learning frameworks
- Experience in model training and optimization
- Ability to implement research papers
- Ability to run experiments
- Ability to iterate quickly on new ideas
- Strong coding skills
- Passion for building reliable, scalable ML systems
Aufgaben
- Design generative 3D machine learning methods
- Develop cutting-edge generative 3D methods
- Create high-quality 3D content from inputs
- Build models for 3D reconstruction
- Train models for 3D reconstruction
- Optimize models for 3D reconstruction
- Evaluate models for 3D reconstruction
- Build models for novel view synthesis
- Train models for novel view synthesis
- Optimize models for novel view synthesis
- Evaluate models for novel view synthesis
- Build models for world generation
- Train models for world generation
- Optimize models for world generation
- Evaluate models for world generation
- Implement state-of-the-art 3D representations
- Experiment with point cloud representations
- Experiment with mesh representations
- Experiment with 3D Gaussian Splatting
- Develop training pipelines
- Develop loss functions
- Improve geometry accuracy
- Improve visual fidelity
- Improve consistency
- Integrate physics-aware priors
- Integrate world model capabilities
- Analyze model performance
- Debug model failure cases
- Iterate rapidly to improve quality
- Iterate rapidly to improve robustness
Berufserfahrung
- ca. 1 - 4 Jahre
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
- deep learning
- generative models
- diffusion models
- transformers
- 3D processing
- camera geometry
- depth
- reconstruction
- point clouds
- meshes
- Gaussian splats
- ML systems
Benefits
Sonstige Vorteile
- Equal opportunity employer
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens SpAItial erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Noch nicht perfekt?
- SpAItial
Research Engineer - Graphics(m/w/x)
Vollzeitnur vor OrtBerufserfahrenMünchen - SpAItial
Research Scientist - World Models(m/w/x)
Vollzeitnur vor OrtKeine AngabeMünchen - DE01 NVIDIA Germany
Research Engineer Neural Reconstruction - New College Graduate(m/w/x)
Vollzeitnur vor OrtBerufserfahrenMünchen - DE01 NVIDIA Germany
Applied Research Engineer, Neural Reconstruction(m/w/x)
Vollzeitnur vor OrtSeniorMünchen
SPSpAItial
Research Engineer(m/w/x)
München
VollzeitVor OrtBerufserfahren
AI/ML
Data Science
Nejo KI-Zusammenfassung
Designing generative 3D ML methods for physically-grounded 3D environment simulation. Expertise in diffusion models and transformers required. Equal opportunity employer.
Anforderungen
- Bachelor's or Master's degree, or equivalent project/research experience
- Degree in computer science, machine learning, computer vision, graphics, robotics, or related field
- Strong fundamentals in deep learning
- Strong fundamentals in generative models
- Expertise in diffusion models and transformers
- Solid understanding of 3D processing concepts
- Knowledge of camera geometry, depth, reconstruction, point clouds, meshes, or Gaussian splats
- Proficiency in Python
- Proficiency in deep learning frameworks
- Experience in model training and optimization
- Ability to implement research papers
- Ability to run experiments
- Ability to iterate quickly on new ideas
- Strong coding skills
- Passion for building reliable, scalable ML systems
Aufgaben
- Design generative 3D machine learning methods
- Develop cutting-edge generative 3D methods
- Create high-quality 3D content from inputs
- Build models for 3D reconstruction
- Train models for 3D reconstruction
- Optimize models for 3D reconstruction
- Evaluate models for 3D reconstruction
- Build models for novel view synthesis
- Train models for novel view synthesis
- Optimize models for novel view synthesis
- Evaluate models for novel view synthesis
- Build models for world generation
- Train models for world generation
- Optimize models for world generation
- Evaluate models for world generation
- Implement state-of-the-art 3D representations
- Experiment with point cloud representations
- Experiment with mesh representations
- Experiment with 3D Gaussian Splatting
- Develop training pipelines
- Develop loss functions
- Improve geometry accuracy
- Improve visual fidelity
- Improve consistency
- Integrate physics-aware priors
- Integrate world model capabilities
- Analyze model performance
- Debug model failure cases
- Iterate rapidly to improve quality
- Iterate rapidly to improve robustness
Berufserfahrung
- ca. 1 - 4 Jahre
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
- deep learning
- generative models
- diffusion models
- transformers
- 3D processing
- camera geometry
- depth
- reconstruction
- point clouds
- meshes
- Gaussian splats
- ML systems
Benefits
Sonstige Vorteile
- Equal opportunity employer
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens SpAItial erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Über das Unternehmen
SpAItial
Branche
Science
Beschreibung
The company is pioneering the development of a frontier 3D foundation model, pushing the boundaries of AI, computer vision, and spatial computing.
Noch nicht perfekt?
- SpAItial
Research Scientist - 3D Diffusion(m/w/x)
Vollzeitnur vor OrtKeine AngabeMünchen - SpAItial
Research Engineer - Graphics(m/w/x)
Vollzeitnur vor OrtBerufserfahrenMünchen - SpAItial
Research Scientist - World Models(m/w/x)
Vollzeitnur vor OrtKeine AngabeMünchen - DE01 NVIDIA Germany
Research Engineer Neural Reconstruction - New College Graduate(m/w/x)
Vollzeitnur vor OrtBerufserfahrenMünchen - DE01 NVIDIA Germany
Applied Research Engineer, Neural Reconstruction(m/w/x)
Vollzeitnur vor OrtSeniorMünchen