Amazon ML Summer School 2026

Amazon ML Summer School aims at providing students the opportunity to gain Machine Learning (ML) skills and which is the first step in becoming ready to build a career in ML. This program is an intensive course on key ML topics and is a great opportunity to learn from and interact with Scientists at Amazon who have immense knowledge in their ML domain. After the successful fifth edition in 2025, the sixth edition of Amazon ML Summer School is being launched for students enrolled in any recognized institute in India and who are expected to graduate in 2027 or 2028. Eligibility: Engineering students enrolled in Bachelor’s/Master’s/ PhD degrees from any recognised institute of India and are expected to graduate in 2027 or 2028 are eligible to enrol in the Amazon ML Summer School. Competition Structure: Round 1: Registrations: Participants are required to register for the competition and submit their resumes. All submitted resumes will undergo a screening process for shortlisting. Highlight your most relevant experience, skills, projects, and achievements. Round 2: SOP Submission: Shortlisted participants must submit a Statement of Purpose (SOP) in PDF format. The SOP should be within 500 words. The framework for SOP submission will be communicated to shortlisted participants at a later stage. Round 3: Selection Test: Shortlisted participants will take a 60-minute selection test. Part A will consist of 20 MCQs covering Machine Learning fundamentals, Probability, Statistics, and Linear Algebra. Part B will include two programming questions to evaluate coding and problem-solving skills. Shortlisted students from the Selection Test will make their way to the Amazon ML Summer School (MLSS) Program. Click here to view the module delivery timelines. Rewards: Chance to secure a spot in the Amazon ML Summer School (MLSS) Program Exclusive Amazon SWAGs Official Acknowledgement Letter from the Amazon Team Networking opportunities with Amazon Scientists and industry experts for top-performing scholars