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Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better?

Amazon Robotics is looking for a talented Applied Scientist in Neurosymbolic AI. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations.

Key job responsibilities
In this role you will:

Work closely with other scientists and engineers, and be part of Amazon’s diverse global science community.
Publish your research in top-tier academic venues and hone your presentation skills.
Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise.

A day in the life
You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale.

About the team
The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.

BASIC QUALIFICATIONS

Experience with programming languages such as Python, Java, C++
Experience building machine learning models or developing algorithms for business application
Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Experience implementing algorithms using toolkits and self-developed code

- - Publications at top-tier peer-reviewed conferences and journals
- - Publication record in generative AI reasoning or classical planning
- - PhD in a relevant field (reinforcement learning, neurosymbolic AI, LLMs for formal reasoning)
- - Experience in reinforcement learning or neuro-symbolic AI
- - Practical experience with PyTorch, the HuggingFace ecosystem, SageMaker, and RL tools

PREFERRED QUALIFICATIONS

- - Experience in professional software development
- - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- - Familiarity with AWS tools and services - including AWS batch, Boto, S3, EC2 etc
- - Strong skills in experimental design/statistical analysis
- - Strong software engineering skills

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

m/w/d

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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