Are you excited about leveraging state-of-the-art Deep Learning, Recommender Systems, Information Retrieval, Natural Language Processing algorithms on large datasets to solve real-world problems?

As an Applied Scientist Intern, you will based in Amazon's Melbourne office working 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.

Please note: This internship is a duration of 6 months full time with a start date in Jan-March 2026.
The successful intern is required to be based in Melbourne and relocation allowance will be provided if you are based outside of Melbourne.

Key job responsibilities
- Develop novel solutions and build prototypes
- Work on complex problems in Machine Learning and Information Retrieval
- Contribute to research that could significantly impact Amazon operations
- Collaborate with a diverse team of experts in a fast-paced environment
- Collaborate with scientists on writing and submitting papers to top conferences, e.g. NeurIPS, ICML, KDD, SIGIR
- Present your research findings to both technical and non-technical audiences

Key Opportunities:
- Work in a team of ML scientists to solve recommender systems problems at the scale of Amazon
- Access to Amazon services and hardware
- Become a disruptor, innovator, and problem solver in the field of information retrieval and recommender systems
- 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

- Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Mathematics, or related field, with specialization in Information Retrieval, Recommender Systems, or Machine Learning
- Strong programming skills, e.g. Python and DL frameworks

PREFERRED QUALIFICATIONS

- Research experience in Deep Learning, Recommender Systems, Information Retrieval, or broader Machine Learning.
- Publications in top-tier conferences, e.g. NeurIPS, ICML, ICLR, KDD, SIGIR, RecSys
- Experience with handling large datasets and distributed computing, e.g. Spark