AmazonPosted 3mo ago

2027 Applied Science Intern (Machine Learning, Recommender Systems), Amazon International Machine Learning

2027 Applied Science Intern (Machine Learning, Recommender Systems), Amazon International Machine Learning at Amazon scores 97 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

AU, VIC, Melbourneinternfull-time

AI in this role

computer-visionnlp

Are you excited about leveraging and extending state-of-the-art Deep Learning, Information Retrieval, Natural Language Processing, Computer Vision algorithms to solve customer problems at the scale of Amazon?

As an Applied Scientist Intern, you will be working in the Melbourne office in a fast-paced, cross-disciplinary team of experienced R&D scientists. You will take on complex problems, work on solutions that leverage existing academic and industrial research, and utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even deliver these to production in customer facing products.

Key job responsibilities

- Develop novel solutions and build prototypes
- Work on complex problems in Deep Learning and Generative AI
- Contribute to research that could significantly impact Amazon operations
- Collaborate with a diverse team of experts in a fast-paced environment
- Present your research findings to both technical and non-technical audiences
- Collaborate with scientists on writing and submitting papers to top ML conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, ACL ICCV, CVPR, KDD.

Key Opportunities:
- Work in a team of ML scientists to solve applied science problems at the scale of Amazon
- Access to Amazon services and hardware
- Potentially deliver solutions to production in customer-facing applications
- Opportunities to be hired full-time after the internship
Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!

Basic qualifications

- Are enrolled in a PhD in computer science, machine learning, engineering, or related fields
- Strong programming skills (Python preferred)

Preferred qualifications

- Experience researching about machine learning, deep learning, NLP, computer vision, data science
- Publications in top-tier conferences such as NeurIPS, ICML, ICLR, AISTATS, ACL ICCV, CVPR, KDD, etc. Please list these publications on your resume.

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 an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionNlp

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision and Nlp. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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