SpAItial is pioneering the development of a frontier 3D foundation model, pushing the boundaries of AI, computer vision, and spatial computing. Our mission is to redefine how industries, from robotics and AR/VR to gaming and movies, generate and interact with 3D content.
We’re seeking a Machine Learning Research Engineer to design, test, and refine high-impact experiments for training our 3D foundation model. You’ll run targeted ablations, implement promising research ideas, and integrate your findings into production‑scale pipelines.
Responsibilities
- Design and run ablation experiments to improve model performance.
- Evaluate new research papers and implement them on our data.
- Incorporate experiment findings into our training pipelines.
- Collaborate with research and engineering teams to maintain strong baselines.
Key Qualifications
- Experience evaluating training runs at different scales (data size, format types, etc.).
- Ability to fine‑tune models with various settings (e.g., ablating different estimators for depth, normals).
- Skill in evaluating research papers and implementing them with our data.
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
- Bachelor’s, Master’s, or equivalent experience in Computer Science, Machine Learning, or a related field.
Preferred Qualifications
- Familiarity with 3D representation learning (e.g., NeRF, Gaussian Splatting).
- Procedural generation toolkit.
- Understanding of generative modeling (e.g., VAEs, GANs, transformers).
- Background in working with large‑scale data such as those used for GenAI training.
- Contributions to open‑source generative AI projects or relevant publications.
At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.
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