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 dataIncorporate

experiment findings into our

training pipelines.Collaborate

with research and engineering teams to maintain strong baselines.Key QualificationsExperience

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 dataProficiency in

Python

and ML frameworks (e.g.,

PyTorch ,

TensorFlow ,

JAX ).Bachelor''s, Master''s , or equivalent experience in Computer Science, Machine Learning, or a related field.Preferred QualificationsFamiliarity with

3D representation learning

(e.g.,

NeRF ,

Gaussian Splatting ).Procedural generation toolkitUnderstanding of

generative modeling

(e.g.,

VAEs ,

GANs ,

transformers ).Background in working with large-scale data and such as those used for GenAI trainingContributions 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.#J-18808-Ljbffr