Member of Technical Staff, Multimodal Reasoning - Applied Science , AGI Autonomy
Amazon - San Francisco, California, USA
Posted Feb 2, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
Member of Technical Staff, Multimodal Reasoning - Applied Science , AGI Autonomy San Francisco, California, USA Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We're enabling practical AI to make our customers more productive, empowered, and fulfilled. Our work leverages large vision language models (VLMs) with reinforcement learning (RL) and world modeling to solve perception, reasoning, and planning to build useful enterprise agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We're entering an exciting new era where agents can redefine what AI makes possible. Key job responsibilities You will lead our efforts to improve the multi-model perception and reasoning abilities of our AI agent in an applied research role. Responsibilities including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff. Basic Qualifications: - 5+ years' experience building machine learning models - PhD or Master's degree in computer science or related field - Proficiency in Python, Java, C++, or related language - Experience with deep learning methods and tools, e.g., PyTorch, JAX Preferred Qualifications: - Background in scientific research with a proven ability to generate and implement new ideas in machine learning - Experience with post-training of large Vision Language Models (VLMs). - Willingness to step outside typical role boundaries to get things done
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