Applied Scientist II, CMT

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Software Engineering

In short

As an Applied Scientist II on the CMT team in Karnataka, India, you will research and develop solutions in machine learning, large language models, and recommender systems. This role involves leading research, mentoring junior scientists, collaborating with engineers on system design, and communicating findings to stakeholders.

Responsibilities

Play a key role in the evolution of Competitive Monitoring systems.
Solve technical challenges in machine learning, large language models in production, and recommender systems.
Act as a thought leader and help set team directions.
Research problem domains and suggest approaches.
Provide critical mentorship to more junior scientists.
Collaborate with engineers on Low-Level Design (LLD) and High-Level Design (HLD).
Communicate findings via writing, internal/external talks, and stakeholder alignment.
Advise junior scientists on their work.
Read current research papers and stay up-to-date on AI research.
Improve CMT models and address stakeholder concerns from a science perspective.
Write Python code.

Requirements

4+ years of experience building models for business applications.
PhD or Master's degree.
Experience in patents or publications at top-tier peer-reviewed conferences or journals.
Experience programming in Java, C++, Python, or a related language.
Experience in areas such as algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high-performance computing.

Desired Qualifications

Experience in state-of-the-art deep learning models architecture design.
Experience in deep learning training and optimization.
Experience in model pruning.
Experience in designing experiments and statistical analysis of results.

Benefits

Inclusive culture that empowers Amazonians.
Workplace accommodations for individuals with disabilities.

Skills

PythonJavaC++Machine LearningDeep Learning

#Applied Science#Machine Learning#Large Language Models#Recommender Systems

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