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REQUIREMENT_ID_286 โ€ข 3-DAY_ACTIVE_POLICY

Associate, ML Data Operations, GO-AI Operations

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 2.5 LPA
Location
Location Virtual (India)
Posted Date
Posted Date Today
Attention to detail video analysis data annotation basic computer proficiency time management ability to work in shifts strong communication problem solving teamwork
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Amazon Placement Papers Open Resource โ†—
A collection of previous placement papers that help candidates understand the type of questions asked in Amazon recruitment.
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Amazon Recruitment Process Experiences Open Resource โ†—
Firstโ€‘hand experiences from candidates who have gone through Amazon's hiring process, useful for preparation.
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Amazon Interview Preparation Guide Open Resource โ†—
Comprehensive guide covering interview formats, common questions, and tips to succeed at Amazon.
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Algorithm Practice Problems Open Resource โ†—
A vast set of coding problems to sharpen problemโ€‘solving skills, beneficial for technical rounds at Amazon.

Bachelor's degree in any discipline (Engineering, Science, Commerce, Arts, etc.) with a minimum of 60% aggregate or CGPA 6.0/10. No active backlogs at the time of joining. Fresh graduates from the 2023, 2024, 2025 or 2026 batches are eligible. Must have a reliable internet connection, a quiet dedicated workspace, and be legally authorized to work in India.

1
Round 1: Online assessment or situational judgement test
2
Round 2: Roleโ€‘specific interview (video auditing simulation and behavioral questions)
3
Round 3: HR interview (culture fit, compensation, availability)
Amazon, founded in 1994, has grown into one of the worldโ€™s most valuable technology companies, offering a vast ecosystem of eโ€‘commerce, cloud computing, digital streaming, and artificial intelligence services. In India, Amazon operates a network of fulfillment centers, logistics hubs, and AIโ€‘driven research labs, employing hundreds of thousands of people across the country. The companyโ€™s culture is built around customer obsession, innovation, and longโ€‘term thinking, encouraging employees to take ownership and deliver results at scale. As part of its relentless push for operational excellence, Amazon continuously invests in automation and dataโ€‘driven decision making to improve inventory accuracy and speed of delivery. The role of Associate, ML Data Operations โ€“ GOโ€‘AI Operations is a contract position (6 months) that sits within the Data Auditing Operations team. The team provides humanโ€‘inโ€‘theโ€‘loop validation for short video clips captured during the stowing process in Amazon Robotics fulfillment centers. Associates watch each 15โ€‘20 second video, interpret the stow action, and mark the exact product location using a proprietary tool. Their judgments feed into machineโ€‘learning models that eventually enable handsโ€‘free, fully automated stowing. The position is fully remote (virtual) but requires a dedicated workspace, a stable internet connection, and the ability to turn on a webcam for virtual meetings. Key responsibilities include: 1. Review and audit several hundred stow videos per shift with a focus on accuracy and speed. 2. Identify product placement details even in lowโ€‘quality or blurry footage. 3. Use the internal annotation tool to mark product locations precisely. 4. Meet daily quality (accuracy) and productivity (videos per hour) targets. 5. Adhere to preโ€‘defined break schedules and ensure 6.8โ€‘7 hours of active video auditing per day. 6. Work rotational shifts, including night shifts, and be flexible to shift changes every 3โ€‘4 months. 7. Maintain a clean, private workspace and ensure data confidentiality. 8. Participate in virtual team meetings, keeping the webcam on when required. 9. Provide feedback to improve the auditing platform and processes. 10. Occasionally report to a physical office for onboarding or training sessions. Tech stack: The role does not require programming skills but familiarity with basic Windows/macOS environments, webโ€‘based annotation tools, and video playback software is essential. A highโ€‘speed internet connection and a webcam are mandatory. Growth path: Highโ€‘performing associates may receive extensions, transition to fullโ€‘time operations roles, or move into dataโ€‘quality analyst positions that involve deeper interaction with machineโ€‘learning pipelines. Amazon also offers internal mobility programs, allowing employees to explore roles in logistics, supplyโ€‘chain analytics, or AI research. Why join Amazon? You get to work on cuttingโ€‘edge automation projects that directly impact millions of customers, gain exposure to largeโ€‘scale data operations, and develop a disciplined work ethic in a fastโ€‘paced environment. The contract also provides a footโ€‘inโ€‘theโ€‘door to one of the worldโ€™s most respected tech giants, with opportunities for future fullโ€‘time conversion and internal career moves.

Amazon โ€” AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC ๐Ÿ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource โ†—
COMPANY GUIDE ๐ŸŽฏ
Amazon Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Amazon and off-campus tech roles.

Open Resource โ†—
MOCK TESTS & PAPERS ๐Ÿ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource โ†—
CODING PRACTICE ๐Ÿ’ป
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource โ†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Attention to detail, video analysis, data annotation, basic computer proficiency, time management, ability to work in shifts, strong communication, problem solving, teamwork) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Amazon Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer โ–ผ
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer โ–ผ
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer โ–ผ
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer โ–ผ
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Amazon as a Associate, ML Data Operations, GO-AI Operations?
Preparation Tip: Highlight Amazon's market reputation, recent tech innovations, and how your skills in Attention to detail directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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