Amazon.com Services LLC

Applied Science, consumer engagement

Sr.AppliedScientist,C360

$100–226k Seattle, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Sr. Applied Scientist, C360 at Amazon.com Services LLC. Skills: Large Language Models, ML solutions, Information Retrieval, NLP. Own scientific roadmap for personalization. Identify high-impact research directions”

Industry & Context.

Applied Science, consumer engagement
Problems you'll solve

Translate ambiguous business problems; Analyze model behavior; Solve real-world problems

What They're Looking For.

Must Have

3+ years ML models business application, Master's degree and 6+ years applied research, Programming in Java, C++, Python, Experience neural deep learning methods, Experience machine learning

Nice to Have

Experience with modeling tools, Experience with large scale distributed systems

What You'll Do.

Own scientific roadmap for personalization

Identify high-impact research directions

Translate business problems into ML formulations

Design end-to-end systems

Conduct offline experimentation

Run production A/B testing

Drive technical decisions on model architecture

Drive technical decisions on training methodology

Drive technical decisions on evaluation frameworks

Balance scientific rigor with business impact

Balance scientific rigor with operational constraints

Raise bar for science team

Conduct design reviews

Establish best practices for experimentation

Establish best practices for reproducibility

Influence cross-functional strategy

Partner with engineering

Partner with leadership

Define product vision

Publish state of the art

Advance state of the art

Contribute to ML community

Engage at conferences

Solve real-world problems

Analyze large amounts of data

Generate opportunities

Develop statistical models

Innovate on behalf of customer

Build features strategically

Mentor junior members

Help junior members grow

How You'll Work.

Team & Collaboration

Partnering with engineering; Partnering with product; Partnering with leadership; Collaboration with other Scientists; Collaboration with Engineers; Collaboration with Product Managers

Communication Scope

Clear communication

Full Job Description

Are you a scientist passionate about advancing Information Retrieval, NLP, and Large Language Models? Do you want access to massive datasets, world-class compute, and a team of top scientists and engineers building the future of e-commerce? If so, you'll be a great fit for our team at Amazon. We build large-scale ML solutions that deliver personalized, up-to-date recommendations to millions of customers. Our team is uniquely positioned to shape how customers think about their shopping journey. We're looking for scientists with deep LLM expertise to build our next generation of models. The team focuses on post-training—instruction tuning, reward modeling, reinforcement learning, and multi-modal alignment. You'll design and run large-scale experiments, analyze model behavior, and develop training recipes that improve core capabilities like reasoning, personalization, and other frontier paradigms. Key job responsibilities - Own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous business problems into well-defined ML formulations - Design and lead end-to-end systems spanning recommendations, information retrieval, and LLM fine-tuning, from problem framing through offline experimentation to production A/B testing and launch - Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact and operational constraints - Mentor and raise the bar for the science team through design reviews, paper discussions, and establishing best practices for experimentation and reproducibility - Influence cross-functional strategy by partnering with engineering, product, and leadership to define the product vision informed by what's technically feasible and scientifically novel - Publish and advance the state of the art — contribute to the broader ML community through patents, publications, and external engagement at conferences A day

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