Tony has provided the following feedback on your session:

Q1: Past project similar to the UberEats merchant API team

• Strengths:
  • Clearly illustrated the user flow and behaviors
  • Connected the work to gRPC calls and offline data pipelines
  • Demonstrated load testing and performance testing (10k QPS)
  • Strong conflict-handling by driving data analysis to prove your stance
  • Results were clear and impact measurable
• Areas for Improvement:
  • Avoid starting with less-relevant details (e.g., data & model side vs service API side)
  • Focus more on technical challenges like traffic, not just project management aspects
  • Listen carefully to the question and answer directly without too many extra words

Q2: Biggest challenge – Spotlight search story

• Strengths:
  • Proactively checked with concerns
  • Highlighted full deployment as an outcome
• Areas for Improvement:
  • Concisely state the challenge in one sentence (e.g., high volume of data ingestion or persistence at scale)
  • Reduce filler phrases like “we design…”, “we try to have a dashboard…”
  • Choose a challenge story that better reflects a truly significant difficulty to match the question intent

Q3: Why do you want to join our team? Next career achievement? Why not continue to work in AI?

• Strengths:
  • Personalized by referencing your own UberEats usage experience
  • Used a real example of bad user experience to strengthen the answer
  • Good understanding of UberEats’ business model and its growth potential
  • Balanced both technical growth and career growth, tying to promotion goals
• Areas for Improvement:
  • None — strong, well-rounded response
  • Clear rationale for why product impact matters in addition to AI focus

Overall Assessment
Jiawei demonstrated strong technical and product insights throughout the session. With more focus on conciseness, sharper articulation of technical challenges, and listening closely to the question intent, Jiawei will be well-prepared for upcoming interviews.