Member of Technical Staff - World Models

Veeda; Toronto; Presencial; CAD 140 000 - 190 000; Tempo integral

Resumo da oferta

Veeda AI is seeking a Member of Technical Staff to advance world models for multimodal, embodied intelligence. You will lead end-to-end research, design scalable generative and predictive models, and collaborate with robotics engineers to deploy reliable capabilities.

The role emphasizes rigorous experimentation, strong PyTorch skills, distributed training, and a track record of publishing or contributing to research in image/video/3D or multimodal domains.

Qualificações

• PhD or equivalent in CS, engineering, or related field.
• Proven ability to train foundation models end-to-end with PyTorch and distributed training.
• Ability to design, run, and analyze ML experiments from hypothesis to conclusion.
• Experience developing evaluations to reveal model limitations.

Responsabilidades

• Design, train, and scale generative and predictive models across visual and spatial modalities.
• Develop methods for multimodal learning and conditioning signals in model behavior.
• Improve sequence modeling for temporal and spatial robustness.
• Explore post-training optimization for quality and efficiency.
• Conduct rigorous evaluations and guided modeling decisions.
• Collaborate with data, systems, and robotics engineers to translate research ideas into reliable capabilities.

Conhecimentos

PyTorch

Experiment design

Model evaluation

Research collaboration

End-to-end training

Formação académica

PhD or equivalent in CS/Engineering

Ferramentas

Distributed training

Descrição da oferta de emprego

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.