Applied Scientist , Gen AI

This job is with Amazon, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

Description

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI and Deep Learning? Join Amazon's AWS Multimodal generative AI science team and be a catalyst for groundbreaking advancements in Computer Vision, Generative AI, and foundational models.

As part of the AWS Multimodal generative AI science team, you'll lead innovative research projects, develop state-of-the-art algorithms, and pioneer solutions that will directly impact millions of Amazon customers. Leveraging Amazon's vast computing power, you'll work alongside a supportive and diverse group of top-tier scientists and engineers, contributing to products that redefine the industry.

Key job responsibilities

• Lead research initiatives in Multimodal generative AI, pushing the boundaries of model efficiency, accuracy, and scalability.
• 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.
  
  

• Ph.D. or Master's in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field.
• Proven expertise in AI/ML fields such as LLMs, Computer Vision, Generative AI, NLP, or foundational models.
• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with cloud-based computing platforms.
• Strong analytical, mathematical, and coding skills (e.g., Python, C++, or Java).
• First author in research publications in peer-reviewed conferences or journals
  
  

• Experience designing and leading complex research projects from ideation to implementation.
• Deep understanding of statistical modeling, optimization, and algorithm development.
• Excellent communication skills, with the ability to convey complex technical information to diverse audiences.