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REQUIREMENT_ID_1358 β€’ 3-DAY_ACTIVE_POLICY

Applied Sciences INTERN

Company
Company Microsoft
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 35,000/month
Location
Location Pan India
Posted Date
Posted Date Sep 26, 2026
Python PyTorch TensorFlow Machine Learning Deep Learning Natural Language Processing Large Language Models Data Cleaning Statistical Analysis Experiment Design Azure Git Docker
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Microsoft interview preparation guide Open Resource β†—
Covers typical coding and aptitude questions asked at Microsoft, helping candidates practice relevant problem‑solving skills.
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Microsoft recruitment experience insights Open Resource β†—
First‑hand accounts of the interview process, useful for understanding the flow and expectations of each round.
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Comprehensive Microsoft interview resources Open Resource β†—
Aggregates tips, sample questions, and interview strategies specific to Microsoft roles.
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Algorithm practice problem set Open Resource β†—
Extensive collection of coding problems to sharpen data‑structures and algorithm skills required for the online assessment.

Currently pursuing a PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Electrical Engineering, Computer Engineering, Econometrics, or a related technical field; must have at least one semester/quarter remaining after the internship; strong research experience in ML, Deep Learning, NLP, Computer Vision, Generative AI or related areas; proficient in Python and ML frameworks such as PyTorch or TensorFlow; experience designing experiments, statistical analysis, and handling large datasets; minimum CGPA/percentage of 7.5/70% (or equivalent) is preferred; no backlog allowed at the time of joining.

1
Round 1: Online coding assessment (Data structures & algorithms)
2
Round 2: Technical interview focusing on ML concepts, research experience, and problem solving
3
Round 3: HR interview covering fit, motivation, and internship logistics
Microsoft is a global technology leader that empowers every person and organization on the planet to achieve more. With a presence in more than 190 countries, Microsoft’s research labs and product teams drive breakthroughs in cloud computing, artificial intelligence, gaming, and productivity software. In India, the company has a strong engineering ecosystem spanning Bengaluru, Hyderabad, and other tech hubs, offering a vibrant mix of multicultural teams, cutting‑edge infrastructure, and a culture of continuous learning. Interns at Microsoft are treated as full contributors; they get access to the same tools, mentorship, and impact opportunities as regular employees, making the internship a launchpad for a future career in technology. The Applied Sciences Internship is designed for PhD candidates who want to translate advanced research into real‑world products. As an Applied Science PhD Intern, you will work side‑by‑side with senior researchers, software engineers, and product managers to build AI‑driven solutions that improve Microsoft’s services. You will be responsible for end‑to‑end development of machine‑learning pipelines, from data ingestion and cleaning to model training, evaluation, and deployment. The role offers exposure to large‑scale data platforms, cloud services, and the latest generative‑AI frameworks, allowing you to see how academic breakthroughs become features used by millions. Key responsibilities include: 1. Design, implement, and evaluate machine‑learning and AI solutions for real‑world product scenarios. 2. Analyse and optimise the performance of advanced algorithms on large‑scale datasets. 3. Translate business and product challenges into research problems and experimental frameworks. 4. Conduct rigorous experiments to assess model effectiveness and drive continuous improvement. 5. Develop, train, fine‑tune, and evaluate Large Language Models (LLMs) and Small Language Models (SLMs). 6. Build scalable prototypes and AI‑powered systems ready for production deployment. 7. Prepare, clean, and analyse massive datasets while ensuring data quality and integrity. 8. Innovate new approaches for model evaluation, benchmarking, and performance optimisation. 9. Collaborate with cross‑functional teams to integrate research breakthroughs into Microsoft products. 10. Document findings, contribute to technical reports, publications, patents, or open‑source projects. Tech stack: Python, PyTorch/TensorFlow, Azure Machine Learning, SQL/NoSQL databases, Git, Docker/Kubernetes, and familiarity with large‑scale data processing tools such as Spark. The internship can lead to full‑time research or engineering roles within Microsoft Research, Azure AI, or product groups, offering a clear growth path from intern to senior researcher or principal engineer. Joining Microsoft gives you access to world‑class mentorship, a collaborative culture that values curiosity, and the chance to work on products that impact billions of users worldwide.

Microsoft β€” AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC πŸ“–
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 β†—
COMPANY GUIDE 🎯
Microsoft Interview Preparation Corner

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

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

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

Open Resource β†—
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 (Python, PyTorch, TensorFlow, Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Data Cleaning, Statistical Analysis, Experiment Design, Azure, Git, Docker) & 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
Microsoft 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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 Microsoft as a Applied Sciences INTERN?
Preparation Tip: Highlight Microsoft's market reputation, recent tech innovations, and how your skills in Python 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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