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REQUIREMENT_ID_330 • 3-DAY_ACTIVE_POLICY

Associate, ML Data Operations, GO-AI Operations

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary ₹6 LPA - ₹8 LPA
Location
Location Remote (India) – Hyderabad, Bangalore, Chennai, Mumbai, AP
Posted Date
Posted Date Yesterday
Attention to detail video analysis data annotation basic computer proficiency time management communication skills ability to work in rotational shifts familiarity with Windows OS reliable internet connectivity
📖
Amazon Placement Papers – Interview Questions Open Resource ↗
Comprehensive collection of past Amazon interview questions that help candidates understand the type of problems asked for operations and data roles.
📖
Amazon Recruitment Process Experiences Open Resource ↗
First‑hand accounts of candidates who have gone through Amazon's hiring stages, useful for preparing for each round.
📖
Amazon Interview Preparation Guide Open Resource ↗
Curated guide covering Amazon's leadership principles, typical interview formats, and tips to ace the assessment.

Bachelor's degree in any discipline; must have a stable internet connection and a quiet, dedicated workspace for remote work.

1
Round 1: Online assessment or screening questionnaire
2
Round 2: Functional interview (video auditing workflow, situational questions)
3
Round 3: HR interview (culture fit, availability, compensation discussion)
Amazon is a global e‑commerce and cloud‑computing giant that has redefined retail logistics through its massive fulfillment network. In India, Amazon operates dozens of fulfillment centers, leveraging cutting‑edge robotics, AI‑driven automation and a relentless focus on customer experience. The company’s culture of "customer obsession" and "invent and simplify" drives continuous innovation, making it one of the most sought‑after employers for fresh graduates. Amazon’s work environment encourages ownership, rapid learning, and the opportunity to impact millions of customers daily. The role of Associate, ML Data Operations – GO‑AI Operations is a contract position (6 months) that supports Amazon Robotics’ fulfillment centers by performing high‑volume video and image audits. Associates watch short stow‑action videos captured on the warehouse floor, use internal annotation tools to verify product placement, and flag any discrepancies. Their work directly feeds into Amazon’s machine‑learning models that automate stowing, thereby improving inventory accuracy and reducing operational defects. The position is primarily remote, with occasional on‑site days for training or team meetings, and requires a dedicated workspace with reliable internet connectivity. Key responsibilities: 1. Review 200‑300 short (15‑20 sec) stow videos per shift and accurately annotate product locations using Amazon’s proprietary tools. 2. Maintain a minimum accuracy threshold (typically >95%) while meeting productivity targets. 3. Identify and flag blurry or ambiguous footage, providing clear comments for further review. 4. Follow standardized SOPs for video auditing, ensuring consistency across the global operations team. 5. Log daily performance metrics and report any systematic issues to the team lead. 6. Adhere to rotational shift schedules, including night shifts, and take pre‑defined breaks to meet the 6.8‑7 hour productive window. 7. Keep the workstation secure; ensure that no unauthorized person accesses work‑related data. 8. Participate in virtual team meetings, keeping the laptop camera on when required. 9. Continuously improve speed and quality by incorporating feedback from coaching sessions. 10. Occasionally travel to a fulfillment center for on‑site training or audits as needed. Tech stack: The role does not require deep programming knowledge but familiarity with Windows OS, web‑based annotation platforms, basic spreadsheet tools, and reliable high‑speed internet is essential. Amazon’s internal video‑review tools are built on proprietary frameworks that integrate with AWS services. Growth path: High‑performing associates can transition to full‑time roles such as Data Analyst, Operations Analyst, or ML Data Engineer within Amazon’s fulfillment technology teams. The exposure to real‑world data labeling and robotics operations provides a solid foundation for careers in AI, supply‑chain analytics, or product management. Why join Amazon? You will be part of a world‑class organization that values data‑driven decision making and offers unparalleled learning opportunities. Even as a short‑term associate, you gain hands‑on experience with cutting‑edge automation technology, receive competitive compensation, and can build a network that opens doors to long‑term careers within Amazon’s vast ecosystem.

Amazon — AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC 📖
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 ↗
COMPANY GUIDE 🎯
Amazon Interview Preparation Corner

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

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

Open Resource ↗
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, communication skills, ability to work in rotational shifts, familiarity with Windows OS, reliable internet connectivity) & 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 ▼
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 ▼
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 ▼
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 ▼
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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