Applied Scientist II , AGI IMAX Science

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI, Computer Vision, and Deep Learning? Join Amazon's AGI IMAX Science team and contribute to significant advancements in Computer Vision, Multimodal Understanding, Generative AI, and foundational models.

As part of the AGI IMAX Science team, you'll lead innovative research projects and train large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models that directly impact millions of Amazon and AWS customers. Leveraging Amazon's vast computing power, you'll work alongside a supportive and diverse group of skilled scientists and engineers, building models and services that make a meaningful difference in the industry.


Key job responsibilities
Lead research initiatives in Computer Vision and Multimodal generative AI, advancing model efficiency, accuracy, and scalability.
Train and fine-tune large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models at scale.
Design, implement, and evaluate deep learning models in a production environment.
Collaborate with cross-functional teams to transfer research outcomes into scalable AWS services.
Publish in top-tier conferences and journals, keeping Amazon at the forefront of innovation.
Mentor and guide other scientists and engineers, fostering a culture of scientific curiosity and excellence.

Basic Qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

- Experience leading, mentoring and growing teams of scientists (teams of five or more scientists)

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