Interview Preparation Guide for Alibaba Mama Algorithm Positions

Hello everyone, I am Xiao Yuan from Alibaba Mama's algorithm team. Based on my own interview experience, I will explain six key aspects that interviewers usually focus on, hoping to help you secure an offer in Alibaba Mama's campus recruitment.

Focus Differences – For campus (intern) and social recruitment, the emphasis differs. In advertising and recommendation algorithms, social recruitment cares more about deep technical experience, breadth of knowledge, and problem‑solving ability (e.g., recall, ranking, mechanisms, and proficiency in C++/Java). Campus recruitment, however, emphasizes fundamental skills and learning ability, such as academic achievements, papers, and coding practice.

Interview Process

1. Unified written test 2. Technical interview round 1 3. Technical interview round 2 4. Final technical interview 5. HR final interview

Resume Writing

What to include? Keep the resume concise (ideally one page). Highlight academic background, published papers, competition awards, solid projects/internships, and open‑source contributions.

What to avoid? Do not list a long string of technologies without depth, include unrelated projects, or add unnecessary personal evaluations.

Pre‑Interview Preparation

1. Environment – Find a quiet, stable‑network place for the online interview. 2. Knowledge – Review common interview topics, especially machine‑learning fundamentals, coding problems, and algorithm basics. 3. Projects – Be thoroughly familiar with every detail of the projects and papers listed on your resume. 4. Attitude – Arrive on time; if technical issues arise, inform the interviewer promptly.

During the Interview

1. Proactively ask questions when you encounter uncertainties. 2. Do not give up; seek hints, explain your thought process, and keep a positive attitude. 3. When asked if you have questions, prepare thoughtful queries about the team’s tech stack, future responsibilities, and any unresolved topics.

Interviewer's Assessment Points (Advertising Algorithm Example)

1. Machine‑learning basics – traditional ML, deep learning, optimization algorithms (e.g., implement K‑means in Python/C++, write formulas for Adagrad/Adam, code a simple logistic regression). 2. Position‑related knowledge – understanding of ad system components such as recall and ranking. 3. Resume‑related questions – deep dive into the projects and experiences you listed.

In summary, prepare thoroughly, master the fundamentals, and be ready to discuss every point on your resume. Treat the interview as a learning opportunity, and you will accumulate valuable experience for your career.