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2024 Applied Science Intern (NSW, VIC, SA, QLD, ACT)
2024 Applied Science Intern (NSW, VIC, SA, QLD, ACT)-March 2024
Perth
Mar 30, 2026
ABOUT AMAZON
Our mission is to be the world’s most customer-centric company.
10,000+ employees
Technology
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About 2024 Applied Science Intern (NSW, VIC, SA, QLD, ACT)

  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.

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

  Adelaide, SA, AUS | Brisbane, QLD, AUS | Canberra, ACT, AUS | Melbourne, VIC, AUS | Perth, WA, AUS | Sydney, NSW, AUS

  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.

  Please note that recruitment for Amazon’s Applied Science internship takes place all year round. Internships start monthly and last 6 months.

  Have a question?

  Please click on the below link to view our FAQs document: https://amazonexteu.qualtrics.com/CP/File.php?F=F_ctP17e4M4BpNzi6

  But if you have any other questions not answered in [email protected]

  Acknowledgement of country:

  In the spirit of reconciliation Amazon acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.

  IDE statement:

  Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer, and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected attributes.

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