Amazon
2024 Applied Science Intern
Our mission is to be Earth's most customer-centric company. This is what unites Amazonians across teams and geographies as we are all striving to delight our customers and make their lives easier, one innovative product, service, and idea at a time.
Banking and investment
Full-time
Office | Sydney, NSW, Australia
Visa sponsorship · No
Entry Level · A role for someone with underlying potential, good motivation and ability to learn. Typically no direct experience is required.
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Why Amazon
The scope and scale of our mission drives us to seek diverse perspectives, be resourceful, and navigate through ambiguity. Inventing and delivering things that were never thought possible isn't easy, but we embrace this challenge every day.
About the role
DESCRIPTION Are you excited about understanding the state-of-the-art Machine Learning, Natural Language Processing, Deep Learning, Computer Vision algorithms, Recommender Systems and designs using large data sets to solve real world problems?
As an Applied Scientist Intern, you will be working in the closet Amazon offices to you (Sydney, Melbourne, Canberra, Adelaide, Brisbane) in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products.
Key job responsibilities Are you excited about using state-of-the-art Deep Learning, Computer Vision, Natural Language Processing algorithms and large data sets to solve real world problems?
A research internship at Amazon is an opportunity to work with leading machine learning researchers on exciting problems using the best tools and hardware in the world. It is an opportunity for PhD students and recent PhD graduates in Computer Vision, Recommender Systems, Deep Learning, Natural Language Processing, and broader Machine Learning to address challenges at a scale that is impossible elsewhere. Along the way, you’ll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver—someone who truly enables machine learning to create significant impact.
As an Applied Scientist Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and building prototypes, you may even deliver these to production in customer facing applications. BASIC QUALIFICATIONS - Currently enrolled or recently graduated from a PhD program in Computer Science, Electrical Engineering, Mathematics, or related field, with specialization in Machine Learning. - Experience in computer vision, recommender systems, deep learning, NLP, or related fields is preferable. - Strong programming skills are essential, and a working knowledge of Python is preferable PREFERRED QUALIFICATIONS - Research experience in Computer Vision, Deep Learning, Natural Language Processing, or broader Machine Learning. - Publications in top-tier conferences such as CVPR, ICCV, NeurIPS, ICML, ICLR, AISTATS, ACL, NAACL and EMNLP. Please state these publications on your resume
What you'll be responsible for
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Investment and Market Research
Conduct research to identify market trends and investment opportunities
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Mathematical Modeling
Develop and use mathematical models and statistical techniques to analyze financial data and make investment decisions
Skills you'll need
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Numerical problem solving
Works with numerical information and performs mathematical calculations to solve problems
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Critical thinking
Identifies and synthesizes patterns and trends amongst various sources of information to reach a meaningful conclusion, perspective or insight
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Attention to detail
Accurately identifies and rectifies discrepancies or errors that exists in information and deliverables