What are the responsibilities and job description for the Research Scientist / Engineer — Foundation Model (Image / Video) position at Luma?
About Luma AI
Luma’s mission is to build multimodal AGI. Through our research on video, 3D, and now multimodal models at Luma, we believe that AI needs to be jointly trained over all signal modalities – text, video, audio, images – analogous to the human brain.
To advance our mission, we build and operate the full stack end-to-end, spanning foundation models, inference systems, and products. This integrated approach powers technologies like Ray3, which is seeing rapidly growing adoption among Fortune 500 companies across media, entertainment, and advertising. Backed by a recent $900M Series C and our partnership with Humain to build a 2 GW compute supercluster (Project Halo), our models and the Dream Machine platform are now enabling creatives worldwide to tell some of the most impactful stories of our time.
Where You Come In
This is a rare and foundational opportunity to define the future of multimodal AI. You will be at the forefront of building and training large-scale multimodal models, directly impacting how users interact with pixels. This role offers the chance to bridge cutting-edge research with magical, shipped products, working end-to-end on novel problems with no existing playbook.
What You'll Do
This opportunity involves both the “science” and “engineering” parts of research, two aspects that are of equal importance.
This is a multi-stack opportunity where you will work on the intersection of modeling, data, systems, and evaluation.
Experience in the following around data, modeling, or evaluation:
Luma’s mission is to build multimodal AGI. Through our research on video, 3D, and now multimodal models at Luma, we believe that AI needs to be jointly trained over all signal modalities – text, video, audio, images – analogous to the human brain.
To advance our mission, we build and operate the full stack end-to-end, spanning foundation models, inference systems, and products. This integrated approach powers technologies like Ray3, which is seeing rapidly growing adoption among Fortune 500 companies across media, entertainment, and advertising. Backed by a recent $900M Series C and our partnership with Humain to build a 2 GW compute supercluster (Project Halo), our models and the Dream Machine platform are now enabling creatives worldwide to tell some of the most impactful stories of our time.
Where You Come In
This is a rare and foundational opportunity to define the future of multimodal AI. You will be at the forefront of building and training large-scale multimodal models, directly impacting how users interact with pixels. This role offers the chance to bridge cutting-edge research with magical, shipped products, working end-to-end on novel problems with no existing playbook.
What You'll Do
This opportunity involves both the “science” and “engineering” parts of research, two aspects that are of equal importance.
This is a multi-stack opportunity where you will work on the intersection of modeling, data, systems, and evaluation.
- Modeling: Architect large-scale multimodal models with a focus on pixel data. Solving challenges in both understanding of and generation of pixels.
- Data: Hillclimbing existing tasks and formulating new tasks through data. Design, implement, and run robust data pipelines for constructing, enriching, and filtering massive pixel datasets.
- Systems: Train large-scale multimodal models on massive datasets and GPU clusters.
- Evaluation: Define and build novel evaluation frameworks to measure pixel intelligence, from understanding (recognition, grounding, spatial-temporal perception...) to generation (realism, consistency, controllability, and human-aligned creative quality).
- Strong foundation in machine learning and foundation models, with experience in image, video, or multimodal domains.
- Deep understanding of autoregressive, diffusion/flow-based, or hybrid/unified models.
- Hands-on experience with PyTorch and large-scale training (distributed, mixed precision, large datasets).
Experience in the following around data, modeling, or evaluation:
- State-of-the-art foundation models in understanding pixels
- State-of-the-art foundation models in generating pixels