Applied Scientist (MT), ASIN Localization in Berlin bei Amazon Development Center Germany GmbH

Amazon Development Center Germany GmbH

• PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML, NLP/Computational Linguistics, mathematics/statistics or related field
• 3+ years of experience working in NLP, ML or data science, building and experimenting models for business application
• Solid programming skills, eg. Python + Pandas stack
• Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Experience working as part of an applied research team
• Superior verbal and written communication skills, ability to convey scientific content to non-experts.

Do you want to drive research that enables the highly scalable, massive throughput machine translation service at the core of Amazon's product localization? Work with MT and LLM technology to improve how we make Amazon available across the globe?

Join the Consumer Localization team and become part of a group of machine learning scientists and engineers dealing with some of the largest scale problems across Amazon. We create automatically billions of product pages on all Amazon marketplaces enabling many millions of customers worldwide to shop in their native language.

This is a unique opportunity at Amazon with great exposure: All of Amazon’s customers worldwide will be your customers. Come join us in our beautiful office in the beating heart of Europe - Berlin.

Key job responsibilities
As part of our team you will be pushing the boundary on applied ML in the localization space. You will

• define and own research projects to ensure we can deliver optimal translation quality for product data at a massive scale
• broaden our vision on how we can apply ML technology to further improve all stages of the product localization process
• help us leverage the potential of latest developments on LLMs and Gen AI to improve customer experience across many languages
• design, experiment and evaluate innovative models to solve end-to-end business problems with ML, and verify the business impact of your solutions
• research and implement novel machine learning approaches and define how we can apply them in our domain
• work with Machine Learning Scientists and Engineers to carry out strategic research and experimentation and implement best practices

A day in the life
Your day at work will be a mix of communication, experimentation, and implementation. You will be discussing with your team and stakeholders (e.g. country teams who use our translations, the service and hosting team, other MT teams at Amazon). This will provide you inputs on requirements that you turn into research questions and experiments, but also insights into the technical options and constraints for productionalizing your research. Your experimental work will be driven by business problems, but you will Think Big to go beyond the needs of the day. So you will be spending as much time on designing experiments and connecting the to the state of the art, as on actual hands on experimentation work. In the implementation part, you will be working with Software and ML Engineers and enable them to turn your research results into production code.

About the team
The team consists of Applied Scientists, Software Engineers and Machine Learning Engineers. Together, this enables us to cover the full space of applied Machine Translation, from open-ended research to practical implementation questions. And it enables us to connect and cooperate with research, business and engineering teams.

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

Berlin, BE, DEU

• 5+ years of experience working in Machine Translation/NLP in a consumer product company
• Demonstrated knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, dialogue systems, information retrieval
• Strong software development skills, track record of turning research ideas into highly scalable implementation
• Ability to map ML performance metrics to business outcomes, and define frameworks for tracking ML systems through business KPIs

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