Technical Staff Engineer - World Models & Multimodal AI

Veeda; California (MO); On-site; USD 130,000 - 200,000; Full time

Job summary

Veeda AI seeks a Member of Technical Staff to advance world models for multimodal physical AI. You will design, train, and scale action-conditioned video and latent dynamics models, and recover pseudo-actions from unlabeled video for scalable training.

The role emphasizes diffusion transformers, memory, and evaluation against real-world robot data. The ideal candidate has a strong background in generative sequence modeling, PyTorch engineering, and end-to-end ML experimentation, with a passion

Qualifications

• Bachelor's degree or equivalent hands-on experience in CS or engineering.
• Experience training generative or predictive sequence models end to end at multi-node scale.
• Ability to independently design, execute, and analyze ML experiments from hypothesis to conclusion.
• Fundamentals of diffusion and flow matching and long-context sequence modeling.

Responsibilities

• Design, train, and scale action-conditioned video and latent dynamics models.
• Condition rollouts on robot actions and camera trajectories with latent actions.
• Train on model rollouts to improve long-horizon stability and memory.
• Fine-tune against physics-grounded reward models and distill to few-step samplers.
• Evaluate simulators for action-following, physical plausibility, and drift.

Skills

Generative models

Sequence models

Experiment design

Diffusion/flow models

Long-context modeling

Reinforcement learning

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch

Job description

Veeda AI seeks a Member of Technical Staff to advance world models for multimodal physical AI. You will design, train, and scale action-conditioned video and latent dynamics models, and recover pseudo-actions from unlabeled video for scalable training.

The role emphasizes diffusion transformers, memory, and evaluation against real-world robot data. The ideal candidate has a strong background in generative sequence modeling, PyTorch engineering, and end-to-end ML experimentation, with a passion