As a Principal Scientist in Amazon’s Artificial General Intelligence division, you will have deep subject matter expertise in the area of large language models and generative AI. You will provide thought leadership on and lead strategic efforts in the personalization of conversational assistant systems, including but not limited to retrieval augmented generation of large language models across a wide range of context providers, privacy and bias/fairness considerations in personalization, work with product, science and engineering teams to deliver short- and long-term personalization solutions that scale to millions of users and a variety of different conversational assistants. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. You will collaborate to design solutions and resolve issues across different organizations at Amazon (e.g. LLM foundational model training and fine-tuning teams, information providers, Amazon businesses like Audible, Kindle and Shopping) to deliver systems at Amazon scale to bring value to billions of Amazon customers. Working across academic partners and in-house experts you will be part of a cutting edge applied research team, and will help to drive this knowledge into our science community through mentoring and knowledge sharing.

Key job responsibilities
You will be a hands on contributor to science at Amazon. You will help raise the scientific bar by mentoring, educating, and publishing in your field. You will help build the scientific roadmap for artificial general intelligence at Amazon scale, leaning into personalization elements. As a key scientist and influencer in the company you will work on the forefront of innovation in AI to apply research to real products. You will be a technical leader in your domain.

About the team
The AGI Personalization team uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal value and relevance to customers interactions.

We are open to hiring candidates to work out of one of the following locations:

Cambridge, GBR | London, GBR

BASIC QUALIFICATIONS

* Graduate degree in Computer science/Math or related field.
* Experience in building complex, real-time systems involving AI, ML, and NLP with successful delivery to customers.
* Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements. Ability to take a project from requirements gathering and design to actual product launch.
* Computer Science fundamentals in data structures, algorithm design and complexity analysis.
* Ability to develop machine learning platform strategies and influence the organization adopting new approaches, concepts and paradigms.
* Exceptional customer relationship skills including the ability to discover the true requirements underlying feature requests, recommend alternative technical and business approaches, and lead science efforts to meet aggressive timelines with optimal solutions.
* Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.

PREFERRED QUALIFICATIONS

* 10+ years of relevant, broad research experience after PhD degree or equivalent.
* Deep and broad expertise across several computer science areas, in particular in Machine Learning and large-scale generative models with a focus on technologies related to conversational AI systems and/or personalization & recommender systems.
* Experience with structured (e.g. knowledge graphs) and/or unstructured knowledge sources.
* Strong core competency in mathematics and statistics.
* Track record of solving complex technical problems.
* Recognized thought leader in your area(s).
* Publications at top-tier peer-reviewed conferences or journals.
* Strong prior experience with mentorship and/or management of senior scientists and engineers.
* Thinks strategically, but stays on top of tactical execution.
* Exhibits excellent business judgment; balances business, product, and technology very well.
* Effective verbal and written communication skills with non-technical and technical audiences.
* Experience working with real-world data sets and building scalable models from large-scale data.