Sr Applied Scientist, Compliance Shared Services

6 months ago

Seattle, United States Amazon.com Services LLC Full time
Amazon's Compliance Shared Services (CoSS) is looking for a smart, energetic, and creative Sr Applied Scientist to extend and invent state-of-the-art research in multi-modal architectures, large language models across federated and continuous learning paradigms spread across multiple systems to join the Applied Research Science team in Seattle. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to deliver scientific innovations into production systems at Amazon-scale that increase automation accuracy and coverage, and extend and invent new research as a key author to deliver re-usable foundational capabilities for automation.

You will analyze and process large amounts of image, text and tabular data from product detail pages, combine them with additional external and internal sources of multi-modal data, evaluate state-of-the-art algorithms and frameworks, and develop new algorithms in federated and continuous learning modes that can be integrated and launched across multiple systems. You will partner with engineers and product managers across multiple Amazon teams to design new ML solutions implemented across worldwide Amazon stores for the entire Amazon product catalog.

Key job responsibilities
•Design and evaluate state-of-the-art algorithms and approaches in multi-modality fusion, large language models, continual learning, and federated learning
•Extend and invent new algorithms and scientific approaches that improve on the state-of-the-art to decrease Amazon’s cost to serve
•Identify and drive scientist productivity improvements across science teams=
•Collaborate with product and tech partners and customers to validate hypothesis, drive adoption, and increase business impact
•Key author in writing high quality scientific papers in internal and external peer-reviewed conferences.
•Lead cross-organization working groups to develop science foundational capabilities applicable to multiple use cases beyond compliance for the broader Customer Trust and Selling Partner Services (SPS) organizations.

A day in the life
- Understanding customer problems, project timelines, and team/project mechanisms
- Proposing science formulations and brainstorming ideas with team to solve business problems
- Writing code, and running experiments with re-usable science libraries
- Reviewing labels and audit results with investigators and operations associates
- Sharing science results with science, product and tech partners and customers
- Writing science papers for submission to peer-review venues, and reviewing science papers from other scientists in the team.
- Contributing to team retrospectives for continuous improvements
- Driving science research collaborations and attending study groups with scientists across Amazon

About the team
We are a combined team of applied scientists and ML engineers looking to not just solve our immediate Compliance problems but also generalize our solutions across Amazon and external customers

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

Seattle, WA, USA

BASIC QUALIFICATIONS

- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with conducting research in a corporate setting
- Proficiency in developing re-usable Python libraries
- Peer-reviewed publications in multi-modality, large language models, continual learning and/or federated learning.
- Ability to convey Machine Learning concepts and considerations to non-experts

PREFERRED QUALIFICATIONS

- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience leading long-term science direction, developing medium-term hypothesis plans, and short-term execution and delegation across 6+ scientists in science teams.
- Significant citations in peer-reviewed scientific contributions in relevant fields
- Practical work experience in building, iterating, deploying and maintaining re-usable python libraries code in end-to-end production systems

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