SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Veeda AI is building multimodal foundation world models for Physical AI. The company is a small, fast-moving team of engineers and researchers from leading AI labs, focused on solving challenging problems at the intersection of AI, robotics, and embodied intelligence.
As a Member of Technical Staff on the World Models team, you will drive research and development across multiple dimensions:
**Core Responsibilities:**
- Design, train, and scale generative and predictive models across visual and spatial modalities
- Develop methods for multimodal learning and control, incorporating diverse data sources and conditioning signals into model behavior
- Improve consistency, robustness, and generalization in models of complex temporal and spatial structure through sequence modeling
- Explore post-training and model optimization techniques to enhance quality, controllability, and computational efficiency
- Develop rigorous evaluations, conduct controlled experiments, and use findings to guide modeling and data decisions
- Collaborate closely with data, systems, and robotics engineers to translate research ideas into reliable capabilities
**Requirements:**
- PhD in Computer Science, Engineering, or related technical field, or equivalent hands-on experience
- Proven experience training machine learning foundation models end-to-end with strong PyTorch skills and hands-on distributed training experience
- Ability to independently design, execute, and analyze machine learning experiments from hypothesis through ablation to conclusion
- Experience developing evaluations that reveal model limitations and inform research decisions
**Nice to Have:**
- Strong publication record or substantial contributions to research on image, video, 3D, or multimodal models
- Experience with learned representations, compression, or tokenization for high-dimensional data
- Experience with conditional generation, temporal consistency, or long-context modeling
- Experience with reinforcement learning, model-based learning, or embodied AI
- Experience with post-training, distillation, or efficient inference for generative models
- Experience working with large-scale, heterogeneous visual or sensor datasets
- Contributions to open-source machine learning projects or research infrastructure
