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We are seeking an Applied Scientist to help build Amazons next-generation customer memory and personalization systems.

As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems.

You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences.
You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage.
You will collaborate closely with engineering and product teams to translate research into measurable customer impact.

Lead backend engineering efforts from rapid prototypes to large-scale applications across products

Leverage and build cloud-based systems to detect targeted attacks and automate cyber threat intelligence production at a global scale

Brainstorm, define and build collaboratively with members across multiple teams

Work in a startup-like environment with a small fast-moving team

Be mentored and mentor other developers on backend, and data storage technologies and our system

Constantly re-evaluate our product to improve architecture, knowledge models, user experience, performance and stability

Be given the autonomy to own your work in a high trust environment

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI, Computer Vision, and Deep Learning? Join our 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 our 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 our 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.

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI, Computer Vision, and Deep Learning? Join our 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.