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Jobs at Veeda AI

6 open roles read nightly from companies’ own hiring pages. Each one links to the original ad.

1–6 of 6 jobs

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  • Veeda AI · Toronto, Ontario, Canada +3

    HybridFull-timeLead / staffData & AI

    ML Methods for Data: Design, develop, and validate ML methods for data selection, enrichment, annotation, and quality assessment.; End-to-End Ownership: Own the full lifecycle, from problem formulation through deployment and continuous improvement.; Large-Scale Application:…

  • Veeda AI · Toronto, Ontario, Canada +2

    HybridFull-timeLead / staff

    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.;…

  • Veeda AI · Toronto, Ontario, Canada +3

    HybridFull-timeLead / staff

    Robotics Systems & Data: Build and maintain robotic systems and data-collection workflows that support research and evaluation.; Real-World Experimentation: Design and run hardware experiments to assess performance, robustness, and generalization.; Evaluation & Analysis:…

  • Veeda AI · Toronto, Ontario, Canada +3

    HybridFull-timeLead / staffData & AI

    Experiment Lifecycle Tracking and Tooling: Design, build and deploy tools that own how a run is defined, launched, resumed, and killed. Build and operate experiment databases for code version tracking, data version tracking, reproducibility, and checkpoint ancestry.; Inference…

  • Veeda AI · Toronto, Ontario, Canada +2

    HybridFull-timeLead / staff

    Simulator integration: Help us connect our world models to existing simulator APIs that customers are familiar with (Isaac Lab, Carla, etc).; Harness engineering: Rethink "classical" to become a physical AI equivalent of an LLM coding harness.; Synthetic data generation: Own…

  • Veeda AI · Toronto, Ontario, Canada +3

    HybridFull-timeLead / staffData & AI

    Distributed Training Throughput: Own step time and model FLOPs utilization for multi-node video world model training, choosing the tensor, context, and expert parallelism mix in PyTorch FSDP2 and Megatron-Core rather than inheriting a default.; Inference Throughput…

Updated 06 Nov 2026 09:37 UTC from the OptiTalent job library.