Member of Technical Staff - World Models

Job summary

Design, train, and scale action-conditioned generative world models for physical AI and robotics. Develop evaluation metrics for physical plausibility and long-horizon stability while fine-tuning models against physics-grounded reward systems.

Job details

Member of Technical Staff – World Models About Us Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one. Responsibilities Model Research & Development: Design, train, and scale generative and predictive models across visual and spatial modalities. Multimodal Learning & Control: Develop methods for learning from diverse data sources and incorporating conditioning signals into model behavior. Sequence Modeling: Improve consistency, robustness, and generalization in models of complex temporal and spatial structure. Post-Training & Efficiency: Explore post-training and model optimization techniques to improve quality, controllability, and computational efficiency. Evaluation & Experimentation: Develop rigorous evaluations, conduct controlled experiments, and use findings to guide modeling and data decisions. Research Collaboration: Work closely with data, systems, and robotics engineers to translate research ideas into reliable capabilities. Requirements PhD degree in Computer Science, Engineering, or a related technical field, or equivalent experience. Experience training machine learning foundation models end to end, with strong PyTorch skills and hands-on experience in distributed training. 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.

What you’ll do

Design, train, and scale action-conditioned generative world models for physical AI and robotics. Develop evaluation metrics for physical plausibility and long-horizon stability while fine-tuning models against physics-grounded reward systems.

Requirements

Requires a bachelor's degree in a technical field and significant experience training generative or predictive sequence models at scale. Candidates must demonstrate fluency in PyTorch and the ability to independently execute machine learning experiments.

Listed skills

• Machine learning · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Generative models
• PyTorch
• Machine learning
• Diffusion models
• Flow matching
• Sequence modeling
• Robotics
• Embodied intelligence
• Video tokenizers
• Latent dynamics
• Reinforcement learning
• 3D Gaussians
• Physics-grounded reward models
• Action-conditioned modeling
• Data experimentation
• Model distillation
• Transformer (Machine Learning Model)
• Recurrent Neural Networks (RNNs)
• Variational Autoencoders
• Planning
• Cardiac Ablation
• Research
• Artificial Intelligence
• Autoregressive Model
• Computer Science
• Machine Learning
• Physics
• Remote Operation
• Simulations
• Trajectory
• Reinforcement Learning
• PyTorch (Machine Learning Library)
• Model Architecture

Job areas

• Technology
• Software
• Engineering
• Science & Research
• Data & Analytics
• Member of Technical Staff
• Artificial Intelligence Engineer (General)
• Software Developers