Amazon Postdoctoral Scientist, The Decision Science & Technology (DST)

The Amazon Postdoctoral Science Program offers recent PhD graduates an exciting opportunity to gain hands-on industry experience, apply their specialized knowledge, and collaborate with Amazon's leading scientists. This program is designed for researchers who have completed their PhD within the past two years and are eager to bridge academia and industry, driving innovation at scale. “This program provides an excellent and fun opportunity to work on cutting-edge customer-obsessed research and learn from and collaborate with some of the brightest researchers and leading experts at Amazon,” said Sareh Nabi, a postdoctoral scientist in Amazon Ads who is participating in a postdoc pilot program. “It also provides a great platform to take the skillset I learned and apply it to a wide range of real-world applications on a large scale.” “This is an opportunity for outstanding researchers to work with us on our hardest scientific challenges,” said Jeremy Wyatt, senior manager of Amazon Robotics AI. “Amazon postdoctoral scientists will pursue exploratory work on our hardest problems, gaining industry experience and publishing the results in the best scientific venues.”
Postdoctoral scientists will contribute to high-impact research initiatives, advance cutting-edge scientific developments, and publish their work in top-tier scientific journals. Participants will benefit from mentorship, access to Amazon's world-class resources, and exposure to real-world challenges with immediate customer impact.

**Focus Areas**

We are seeking candidates with expertise in foundational and applied research, particularly in:
- Large Language Models (LLMs): Experience developing, fine-tuning, and scaling LLMs for diverse applications.
- Graph Databases: Strong understanding of graph theory, data modeling, and implementation of graph database technologies in complex systems.
- Mathematical Optimization: Good understanding of mathematical optimization, including mixed-integer optimization, combinatorial optimization, and robust optimization.

**Basic Qualifications**

- PhD in a relevant field (e.g., Computer Science, Operations Research, Mathematics, Statistics) received within 2 years of program start date.
- Proven publication record in areas such as LLM, NLP, Generative AI, Graph Database, or other relevant domains.
- Hands-on experience with technologies related to large language models, graph databases, and mathematical optimization.


Preferred qualifications

Postdocs demonstrate the following preferred job qualifications:
- Ability to independently deliver results in a fast-paced environment
- Publications at top-tier, peer-reviewed conferences and/or journals such as NurIPS, ICML, ICLR, CIKM, ICDE
- Exceptional verbal and written communication skills - Expert knowledge in modeling and performance, operationalization, and scalability of scientific techniques and establishing decision strategies

Required application materials:
- CV, which lists all peer-reviewed publications and conferences
- Research statement that outlines your research achievements and future research interests, and
- A journal article or book chapter that demonstrates your domain expertise- PhD in a relevant field (e.g., Computer Science, Operations Research, Mathematics, Statistics) received within 2 years of program start date.
- Proven publication record in areas such as LLM, NLP, Generative AI, Graph Database, or other relevant domains.
- Hands-on experience with technologies related to large language models, graph databases, and mathematical optimization.
• Experience in data science and quantitative research
• Proficiency in technologies relevant to the subfield
Postdocs demonstrate the following preferred job qualifications:
- Ability to independently deliver results in a fast-paced environment
- Publications at top-tier, peer-reviewed conferences and/or journals such as NurIPS, ICML, ICLR, CIKM, ICDE
- Exceptional verbal and written communication skills - Expert knowledge in modeling and performance, operationalization, and scalability of scientific techniques and establishing decision strategies

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