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Vakances uzņēmumā

Staff Generative AI Researcher, World Models

Lead research into diffusion-based world models for General Motors’ autonomous vehicles. Advance generative AI, simulation, and Level 3 autonomy through large-scale research.

ILIzraēlaHibrīdiPilna slodzeVadošais speciālistsNepieciešamā Valoda: angļuMākslīgais intelekts
PythonPyTorch

Apraksts

  • Conduct original research on diffusion-based world models of driving environments and dynamics.
  • Design, train, and evaluate large-scale diffusion models for video and multimodal generation.
  • Prioritize temporal consistency, physical plausibility, and controllability in generated outputs.
  • Build and optimize large-scale training across distributed, multi-GPU systems.
  • Improve training stability, throughput, memory use, and scalability.
  • Create action- and context-conditioned world models for future scenarios, counterfactual exploration, closed-loop simulation, and long-tail scenario coverage.
  • Develop and assess data and training approaches for fleet-scale datasets, including curation, pretraining, fine-tuning, and targeted generation of challenging driving scenarios.
  • Use rigorous evaluations to connect generative quality, consistency, and scenario coverage with downstream autonomous-driving KPI gains.
  • Work with engineering teams to turn research into scalable tools for simulation, synthetic data generation, and autonomy development.
  • Publish research in leading conferences and journals.
  • Monitor generative AI advances and develop technologies relevant to Level 3 autonomous driving.

Prasības

  • PhD in Computer Science, Electrical Engineering, Robotics, or a related discipline; exceptional M.Sc. graduates may also be considered.
  • More than three years of research experience in generative AI, computer vision, machine learning, or a related field.
  • Evidence of deep expertise and meaningful contributions to complex research projects.
  • Strong diffusion-model expertise and practical experience designing, training, and evaluating generative models for images, video, or multimodal data.
  • Practical experience training deep learning models at scale across distributed systems, including stabilizing training, optimizing performance, and conducting systematic experiments.
  • Strong grasp of world modeling and spatiotemporal learning, including temporal consistency, conditioning methods, and learning environmental dynamics.
  • Strong publication history in leading AI/ML conferences and journals.
  • Excellent Python programming skills.
  • Proficiency with modern AI frameworks, including PyTorch.

Priekšrocības

  • Role-related assessments and/or pre-employment screening may be required.
  • Reasonable accommodations are available to applicants with disabilities.

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