JobgetherRemote · Canada
2h ago
AmazonPosted 3mo ago
L4
2027 Applied Science Intern (Computer Vision), Amazon International Machine Learning
2027 Applied Science Intern (Computer Vision), Amazon International Machine Learning at Amazon scores 96 out of 100 on AI centrality, which makes it a Level 4 role on this board.
AU, VIC, Melbourneinternfull-time
AI in this role
computer-vision
As an Applied Scientist Intern, you'll work in a fast-paced, cross-disciplinary team of pioneering researchers. You'll tackle complex problems, developing solutions that either build on existing academic and industrial research or stem from your own innovative thinking. Your work may even find its way into customer-facing products, making a real-world impact.
Please note: This internship is a duration of 6 months full time with a start date in Jan-March 2027.
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 Computer Vision and Machine Learning
- Contribute to research that could significantly impact Amazon's operations
- Collaborate with a diverse team of experts in a fast-paced environment
- Collaborate with scientists on writing and submitting papers to Tier-1 conferences (e.g., CVPR, ICCV, NeurIPS, ICML)
- Present your research findings to both technical and non-technical audiences
Key Opportunities
- Collaborate with leading machine learning researchers
- Access Amazon tools and hardware (large GPU clusters)
- Address challenges at an unparalleled scale
- Become a disruptor, innovator, and problem solver in the field of computer vision
- Potentially deliver solutions to production in customer-facing applications
- Opportunities to become an FTE 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 Computer Vision or Machine Learning
- Experience in computer vision or related fields
- Strong programming skills (Python preferred)
Preferred qualifications
- Research experience in Computer Vision, Deep Learning, or broader Machine Learning.
- Publications in top-tier conferences such as CVPR, ICCV, NeurIPS, ICML, ICLR, ECCV, 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 Vision
Questions you could be asked
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- 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. 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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