This time, we are sharing a participant's Doordash VO's real life experience. The three rounds of interviews were moderately difficult, but there were a lot of details. Luckily, our programhelp helped him to grasp the key points and get the offer smoothly, so let's review the interviews by rounds:
Background of trainees
The participant is a master's student, specializing in Data Science instead of traditional CS, so he has a relatively weak background in system design and large-scale distributed architecture.
In the preparation stage, he had brushed up on a lot of Leetcode, but lacked experience in real business coding questions, such as those involving geographic coordinates and takeaway scenarios.
On the behavioral side, he himself admits that he is a bit weak in "storytelling". He can usually write a report, but his verbal expression is not concise enough, and he tends to stray from the topic.
So, he came to us at programhelp, hoping to get a real-time addition to his technical depth + presentation logic.
Round 1: Coding - Recommended Restaurants Nearby
Students get the topic is to write a function to find a nearby restaurant, the logic is not complicated, but stuck in the distance calculation piece. Many students will subconsciously use European distance, but in real business, we have to consider geographic coordinates (latitude and longitude). He was a bit nervous, and we immediately reminded him to use the haversine formula to calculate the distance between two points on the earth's surface.
In addition, when the interviewer asked about algorithm optimization, the participant only talked about "sorting by sort" at the beginning, and we hinted that he could mention spatial index structure (such as KD-Tree) to speed up the filtering. The interviewer obviously nodded once this point was raised, which was a plus.
Round 2: System Design - Takeaway ETA Forecasting
This round of the test is about designing a time of delivery (ETA) system. Participants initially thought of restaurant preparation time + distance traveled, but ignored traffic fluctuations and dynamic update mechanisms. We voice alerted at critical moments:
- Plus input of traffic/weather/peak hour data;
- Explain how the system dynamically updates the ETA and notifies users and merchants in real time.
The trainee followed this line of thought and added the phrase "trade-off between accuracy and system complexity" to give more depth to the answer, and the overall effect was very good.
Round 3: Behavioral Surfaces - Technical Challenge Review
Behavioral interviews are the weak point of many students, and it is easy to be too scattered. The participant initially wanted to talk directly about the program experience, and we immediately guided him to apply STAR Law:
- Situation: complex distributed task scheduling project
- Task: High system latency due to performance bottlenecks
- Action: Identify bottlenecks through profiling, refactor core logic, and introduce caching mechanisms
- Result: system latency reduced by 40% and internal best practice documentation developed
When you speak in this way, the logic is clear, the effectiveness is clear, and it also makes the interviewer comfortable to listen.
Overall Experience & Psychological Dimension
On the day of the interview, he was actually very nervous at first, especially during the first round of coding, his hands were shaking a little bit when he was knocking out the code, fearing that he would get stuck. We reminded him in real-time voice in the background, "Write out the basic functions first, then fill in the boundaries, don't panic." These words immediately stabilized him.
In the second round of system design, at first, he only thought of the preparation time and distance of the restaurant, and his speech was very thin, but when we pointed out the sentence "add transportation and dynamic updates", he immediately expanded into a complete answer, and his confidence increased significantly.
In the third round of behavioral interview, he was worried that he could not express himself well, but after we reminded him of the STAR framework, he was able to make his fragmented experience clear. When he came to the result part, he sounded a bit excited and the interviewer laughed with him.
Throughout the three rounds down, he said he felt like "open hang-up", from nervous to comfortable, from short answers to be able to take the initiative to extend, and finally even felt that the end of the "original interview can also be so smooth".
To be honest, many students do not know how to do it in the real world, but when they are nervous, they tend to forget the details, give thin answers, and even run out of things to say on the behavioral side. This is also the significance of the existence of our programhelp - the key moment to point out the ideas, remind to add, so that students no longer fall off the chain.
How can Programhelp help you?
OA Writing / Written Exam: Specialized in providing Online Assessment (OA) ghostwriting services for HackerRank, Oxfam, Codesignal and other platforms, remote and untraceable operation, 100% test cases pass, no charge if not passed.
Interview assistance / VO assistsThe interviews of Coding, System Design, and Behavioral are comprehensively covered, and the effect is far more than that of ordinary AI.
Interviews / Full escort:Our professional team can help you to complete the interview by transferring the camera and voice changing technology, and simulate in advance in the way of lip-synching, so as to cooperate with each other and help you to reach the Offer directly.
wrap-around service:We support a small amount of deposit, and then pay the final payment after you get the offer, which is safe and reliable.
More Customized Services: One-on-one support is available for mock interviews, resume packaging, algorithm tutoring, Quant interview assists, and international student admissions proxy interviews.
Whether you're targeting Doordash or FAANG, a major school recruiter, or a top Quant, we can customize a plan to help you save time, avoid pitfalls, and get straight to the Offer.
