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.
ResponsibilitiesProduct-Driven Research: Push the frontier on generative world models.
Foundation Models: Advance image, video, and 3D generation architectures. You will design, train, and iterate on the core models that power creation pipeline across modalities.
Post-Training: Develop RLHF, DPO, reward modeling, and alignment techniques that give creators fine-grained control over aesthetics, anatomy correctness, and stylistic consistency.
Data Curation at Scale: Build and refine large-scale data pipelines — sourcing, filtering, labeling.
Evaluation & Measurement: Design rigorous evaluation methodologies for aesthetics, controllability, correctness, and creator usefulness, moving beyond FID to metrics that actually matter for production.
You have a Bachelor's degree or equivalent hands-on experience in Computer Science, Engineering, or a related technical field.
You have strong hands-on experience with deep learning frameworks such as PyTorch, and can move fluently from prototype to production-scale training.
You can independently design, execute, and analyze machine learning experiments — from hypothesis to ablation to conclusion.
You have a solid understanding of machine learning fundamentals and modern deep learning methods — optimization, generalization, architectures, and the intuition to know when theory breaks down in practice.
You have built or maintained scalable data curation pipelines for ML training data.
You have published or contributed to research on generative models for image, video, or 3D content.
You have experience fine-tuning large language models.
You have experience with research on LLM agents.
You have experience with reinforcement learning.
You have operated large-scale distributed training workflows (multi-node, multi-GPU).
You have contributed to open-source generative model projects or related infrastructure.
