Lyft Machine Learning Engineer (Staff Level) Interview Preparation Guide
Machine Learning Engineer
Lyft
Staff
8 rounds
Updated 11/23/2025
Lyft's Machine Learning Engineer interview process for Staff level candidates is comprehensive and spans multiple weeks. It evaluates technical depth in machine learning systems, production-scale thinking, system design expertise, and leadership capabilities. The process combines live coding assessments, complex system design problems, real-world case studies, and behavioral evaluations to identify candidates who can architect scalable ML solutions and guide cross-functional teams.
Interview Rounds
1
Recruiter Screening
30 min4 focus topicsbehavioral
2
Technical Phone Screen 1: Machine Learning & Algorithms
60 min5 focus topicstechnical
3
Technical Phone Screen 2: System Design & Real-time Data Processing
75 min5 focus topicssystem design
4
Onsite Interview 1: Deep Learning & Model Optimization
90 min5 focus topicstechnical
5
Onsite Interview 2: ML Systems Design & Architecture
90 min6 focus topicssystem design
6
Onsite Interview 3: Real-world Case Study & Problem-Solving
90 min6 focus topicscase study
7
Onsite Interview 4: Advanced System Design - Lyft-Specific Challenges
90 min5 focus topicssystem design
8
Onsite Interview 5: Behavioral & Cultural Alignment
60 min6 focus topicsbehavioral
Additional Information