Applied Scientist , Gen AI
DESCRIPTION
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
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.
About the team
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture:
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
BASIC QUALIFICATIONS
* 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.
PREFERRED QUALIFICATIONS
* 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.