Recruiter screen, technical phone (stats + SQL + Python), virtual onsite with case study on an abuse detection problem, ML deep-dive, behavioral, and final with hiring manager. Trust team operates at LinkedIn-scale data.
Walk me through an analysis that changed a product decision.
Tell me about a model you deprecated. Why?
Describe partnering with engineering on a real-time ML feature.
How do you balance precision and recall for an abuse-detection model?
Account Executive
virtual
· Difficulty 3/5
Recruiter screen, video with sales manager, mock customer call, behavioral with cross-functional partners, and final with regional director. LinkedIn Sales has a strong consultative culture and onboarding is structured.
Walk me through your largest enterprise deal.
Tell me about a customer that nearly churned. How did you retain them?
Describe a situation where the buyer changed mid-deal.
How do you stay credible when the customer knows your product better than you on day one?
Machine Learning Engineer
virtual
· Difficulty 5/5
Very rigorous process. Phone screen, then ML-specific technical screen. Virtual onsite had 5 rounds: ML system design (feed ranking), coding, ML fundamentals, applied ML (feature engineering), and behavioral. They tested both breadth and depth of ML knowledge.
Tell me about a ML system you designed from scratch. What were the key decisions?
Describe a time you improved model performance significantly. What was your approach?
How do you think about feature engineering for social network data?
Give an example of collaborating with researchers to productionize a new technique.