2020–2026 Microsoft CoreAI and AI/ML Intern OA / Interviews: 118 Experiences (Part 8 :)

This part preserves 16 Microsoft AI/ML internship reports from 2020.

Coverage: 2020 · candidate reports 092–107 · phone screens and final loops

Series note: Part 1 focuses on the CoreAI / AI-ML Software Engineering Intern loop. This part preserves other Microsoft AI/ML internship reports without treating them as CoreAI SWE evidence.

2020 reports (continued)

092: Applied & Data Scientist PhD Intern — final interview loop

Report date: 2020-11-15 · Role: Applied & Data Scientist PhD Intern · Stage: Final loop / onsite · Interview date: 2020-10

Detailed Microsoft Applied & Data Scientist PhD internship offer report.

1. Evaluate a Windows Notification System. Design an end-to-end evaluation for Windows system notifications. Use business judgment to choose features and metrics and propose a model. Expect follow-ups based on the proposed approach.
2. Model User Preferences and Segment Customers for a Photo Product. For a photo-like product, explain how to understand user preferences. Design a customer-segmentation approach.
3. Classify Strings as Valid or Garbage. Build a statistical model that classifies a string as valid or garbage. Cover the process from data collection through the statistical model. Discuss hypothesis testing and probability within the case.

Other reported topics or limits: Screen and Onsite Behavioral or Conceptual Questions.

093: Data Scientist PhD Intern — final interview loop

Report date: 2020-11-13 · Role: Data Scientist PhD Intern · Stage: Final loop / onsite

Concise Microsoft Data Scientist PhD internship virtual-onsite report.

1. Use a Dataset to Help a Sales Department. Given a dataset, propose what analyses or models can be built. Explain how a data scientist could use it to help a sales department.

Other reported topics or limits: Undisclosed SQL Table Manipulation; Regularization, Missing Values, and Behavioral Discussion.

094: Data & Applied Science PhD Intern — final interview loop

Report date: 2020-11-09 · Role: Data & Applied Science PhD Intern · Stage: Final loop / onsite

Detailed Microsoft Data & Applied Science PhD internship offer report.

1. Quantify Pure Causal Effects for a Mobile Product. Choose a frequently used mobile application and answer high-level product-analytics questions about it. Explain how to quantify pure causal effects, using ideas such as A/B testing or causal inference.
2. Model What Drives Free-Trial Users to Purchase. Determine what drives users from free trials to purchase. Formulate the task as classification and consider logistic regression. Specify the data and answer model follow-ups such as regularization.

Other reported topics or limits: Screen Behavioral and ML Background Questions; Unspecified A/B Testing and Easy SQL Items.

095: Data & Applied Scientist PhD Intern — final interview loop

Report date: 2020-11-03 · Role: Data & Applied Scientist PhD Intern · Stage: Final loop / onsite · Interview date: 2020-11-02

Detailed Microsoft Data & Applied Scientist PhD internship virtual onsite.

1. Duplicate Zeros. The coding task was LeetCode 1089, Duplicate Zeros.
2. Diagnose and Model User Churn. Given a scenario in which users churn, explain why they are leaving. Develop the analysis strategy step by step and discuss the proposed model's pros and cons.
3. Match Anonymous Records from the Same Person. Given anonymized data, determine which records originate from the same person.
4. Allocate Resources by Demand Density. Allocate resources according to the density of demand. Define the target, how it will be measured, and how the allocation will be optimized.

Other reported topics or limits: Phone-Screen Probability, P-Value, and Forgotten Product Question; Undisclosed SQL Follow-Up After Duplicate Zeros.

096: Data & Applied Sciences PhD Intern — phone or technical screen

Report date: 2020-10-27 · Role: Data & Applied Sciences PhD Intern · Stage: Phone / technical screen

Detailed Microsoft Data & Applied Sciences PhD internship screen for the 2021 cycle.

1. Measure the Incremental Value of Desktop and Mobile Clients. A Microsoft product is available on the web, a desktop client, and a mobile client. Determine whether the desktop and mobile clients create incremental product value. Specify the experimental procedure and explain statistical testing and p-values.

Other reported topics or limits: Resume and PhD Experience Discussion.

097: Data & Applied Science Intern — phone or technical screen

Report date: 2020-10-15 · Role: Data & Applied Science Intern · Stage: Phone / technical screen · Interview date: 2020-10-13

Microsoft Data & Applied Science internship phone screen. The six reported prompts covered prior ML projects, improving a favorite Microsoft product, team preference, explaining two familiar deep-learning models in depth, a difficult teamwork experience, and explaining AI to a five-year-old.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: Behavioral, Product-Preference, and ML-Explanation Screen.

098: Data & Applied Scientist PhD Intern — phone or technical screen

Report date: 2020-10-06 · Role: Data & Applied Scientist PhD Intern · Stage: Phone / technical screen · Interview date: 2020-10-06

Detailed Microsoft Data & Applied Scientist PhD internship first-round video interview.

1. Design Storage for Big Data. Explain how to handle storage for a large data workload.
2. Design a Ranking System. Explain how to build a ranking system.

Other reported topics or limits: Foundational ML Concept Questions; Talk About Microsoft Products.

099: Data & Applied Sciences PhD Intern — phone or technical screen

Report date: 2020-09-29 · Role: Data & Applied Sciences PhD Intern · Stage: Phone / technical screen

Detailed Microsoft Data & Applied Sciences PhD internship recruiter screen. Thirteen reported prompts covered DS-versus-ML preference, project depth, coding and language background, data types, NLP/deep-learning model experience, teamwork, explaining deep learning to a child, publications, technology passion, product improvement, motivation, and team preference.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: Recruiter Background, Behavioral, and Product-Preference Screen.

100: Data Science PhD Intern — phone or technical screen

Report date: 2020-09-28 · Role: Data Science PhD Intern · Stage: Phone / technical screen · Interview date: 2020-09-28

Detailed Microsoft Data Science PhD internship first-round screen for the 2021 cycle. The questions covered ML/NLP/DL projects, project difficulty and pride, teamwork, model evaluation, explaining deep learning to a child, interests, motivation, product improvement, and team preference. No reusable standalone artifact was requested.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: ML, NLP, DL, Project, Teamwork, and Product Screen; Speculative Undisclosed Second-Round Topics.

101: Data Science PhD Intern — phone or technical screen

Report date: 2020-09-25 · Role: Data Science PhD Intern · Stage: Phone / technical screen · Interview date: 2020-09-25

Detailed Microsoft data-science PhD internship first-round phone screen. The questions covered ML background, research, behavioral examples, product preference, motivation, and team fit rather than concrete technical tasks.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: General ML Background, Behavioral, and Product Screen.

102: Microsoft Research Redmond Machine Learning Intern — phone or technical screen

Report date: 2020-09-23 · Role: Microsoft Research Redmond Machine Learning Intern · Stage: Phone / technical screen

process summary from a returning Microsoft Research Redmond intern working on graph neural networks. Both interviews were research conversations about the candidate's papers and group projects, with no coding exercise.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: Paper and Group-Project Research Discussion.

103: Data & Applied Sciences PhD Intern — phone or technical screen

Report date: 2020-09-16 · Role: Data & Applied Sciences PhD Intern · Stage: Phone / technical screen

Detailed Microsoft Data & Applied Sciences PhD internship technical phone screen.

1. Design a Self-Driving Car Perception System. Design a perception system for a self-driving car. Formulate the task as a machine-learning problem. Specify the training data and evaluation metric. Explain how to handle imbalanced data. Discuss how the approach changes when detecting bicycles or trucks instead of cars.

Other reported topics or limits: Self-Introduction, Internship, and Research Background.

104: Machine Learning Intern — final interview loop

Report date: 2020-04-24 · Role: Machine Learning Intern · Stage: Final loop / onsite

Detailed Microsoft Machine Learning internship offer report.

1. Generate a Fibonacci-Style Sequence with Constant Space and a Mathematical Solution. Generate a sequence in which each third/current value is the sum of the previous two values. The candidate gave a two-pointer constant-space solution and also a mathematical solution.

Other reported topics or limits: Design a Face-Recognition Neural Network Under Evolving Requirements; ML Theory, Research, Probability, and Behavioral Discussion.

105: Data Scientist Intern, Yammer team — phone or technical screen

Report date: 2020-04-19 · Role: Data Scientist Intern, Yammer team · Stage: Phone / technical screen

Microsoft Data Scientist internship phone screen for the SF Yammer team. The four questions focused on prior DS experience, explaining DS to a child or nontechnical audience, languages, and packages; the author described communication and logical expression as the emphasis.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: Data-Science Background and Communication Screen.

106: Microsoft Research Systems Intern — phone or technical screen

Report date: 2020-03-28 · Role: Microsoft Research Systems Intern · Stage: Phone / technical screen · Interview date: 2019-12-16

MSR Systems internship pass report, published in 2020. The single Skype loop involved two researchers and was entirely about research, papers, and the candidate's next project steps; the author explicitly reports no coding.

1. No standalone technical prompt was preserved. This entry is retained for its process, timing, role, or interview-format evidence.

Other reported topics or limits: Discuss Research, Papers, and Next Project Progress.

107: Microsoft Research + AI Research Intern — final interview loop

Report date: 2020-02-28 · Role: Microsoft Research + AI Research Intern · Stage: Final loop / onsite

Detailed Microsoft Research + AI internship video interview.

1. Implement Softmax Cross-Entropy. Implement softmax cross-entropy with NumPy. The stated input is logits and the output is loss. The interviewer may also ask for a PyTorch implementation.

Other reported topics or limits: Candidate Paper and Research Deep Dive.

Part 9 continues with the next candidate reports.