HR Call The HR call focused on my background, motivation for applying, and interest in the company. We discussed my career goals, cultural fit, work authorization, and expectations around salary, role scope, and timeline.
1st Coding (2 Tasks) The first coding round involved two algorithmic tasks under time constraints. Both required efficient problem-solving and clean implementation. I explained my approach, optimized for edge cases, and demonstrated proficiency with Python data structures.
2nd Coding (2 Tasks) The second coding round included more challenging tasks involving algorithmic reasoning and optimization. I was asked to justify trade-offs, improve complexity, and handle edge scenarios. Clear communication and structured thinking were essential throughout.
Behavioral In the behavioral interview, I was asked about teamwork, conflict resolution, leadership, and ownership. I shared concrete examples using the STAR framework to highlight impact, adaptability, decision-making, and growth in past professional experiences.
ML System Design The ML system design round required structuring an end-to-end pipeline for a real-world problem. I covered data collection, preprocessing, feature engineering, model selection, deployment, monitoring, scalability, trade-offs, and ethical considerations to ensure robustness.
Coding:
Implement an LRU cache.
ML System Design:
Design a news feed ranking system.
The following metrics were computed from 1 interview experience for the Meta Machine Learning Engineer role in London, England.
Meta's interview process for their Machine Learning Engineer roles in London, England is extremely selective, failing the vast majority of engineers.
Candidates reported having very good feelings for Meta's Machine Learning Engineer interview process in London, England.