Research Scientist, World Models

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

89% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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About this role

Research Scientist, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which aims to deliver realistic, scalable, controllable, and efficient simulation. As a Research Scientist in World Models, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Conduct fundamental and applied research in generative and predictive world-modeling • Video generation and prediction. • Latent diffusion / autoregressive / flow-matching models. • Multimodal foundation models for driving scenes. • LLM / VLM / VLA methods for scene understanding, reasoning, and control. • Generative scenario modeling and controllable simulation. • Model distillation. - Collaborate with

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