Senior Research Scientist (World Action Modeling)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Senior Research Scientist (World Action Modeling): Designing and implementing generative world action modeling solutions to simulate future world states and generate policies for autonomous vehicles with an accent on scalable data pipelines and evaluation strategies. Focus on scaling efficacy, model architecture design ablations, and conducting cutting-edge research in world models.
Location: Hybrid; must be based in Mountain View, San Francisco, New York City, or Kirkland (USA)
Salary: $213,000—$263,000 USD
Company
is an autonomous driving technology company with the mission to build the world's most trusted driver to improve mobility and safety.
What you will do
- Design and implement generative world action modeling solutions for autonomous vehicle policies.
- Develop and maintain scalable data pipelines to process information from multiple sources.
- Design and implement evaluation strategies and analyze model behaviors, including scaling efficacy and architecture ablations.
- Conduct cutting-edge research and share findings via technical blog posts, reports, and academic publications.
- Collaborate across and Alphabet to apply developed techniques to real-world products.
Requirements
- PhD in Computer Science or similar discipline, or equivalent deep learning research experience.
- 5+ years of experience with Deep Learning and Generative Models.
- Strong coding skills in Python and familiarity with JAX, TensorFlow, or PyTorch.
- Experience in large-scale distributed training and various forms of parallelism.
- Expertise in generative models (diffusion, autoregressive) for world models, video, 3D, or simulation.
- Must be based in one of the designated US locations.
Nice to have
- Publications at top-tier conferences such as CVPR, ICCV, ECCV, ICLR, ICML, or NeurIPS.
- Contributions to high-impact industry AI projects.
- Direct experience training large world-action models.
- Familiarity with Reinforcement Learning in simulation environments.
Culture & Benefits
- Hybrid work schedule.
- Eligibility for a discretionary annual bonus program.
- Equity incentive plan.
- Comprehensive company benefits program.
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