Amazon
Mid
Build the ML-powered discovery systems behind what Prime Video shows you
Join Prime Video's Discovery Engineering team to build the low-latency ranking and retrieval systems, offline ML pipelines, and LLM-based evaluation systems that decide what customers see when they open Prime Video. Requires production ML integration experience and comfort owning distributed systems end to end.
Step into this interview
Free · a live voice mock calibrated to this exact role
Likely format
Amazon SDE loop: online assessment, phone screen, then onsite/virtual loop of multiple rounds covering coding, system design, and Leadership Principles behavioral interviews.
What this interview tests
- Low-latency recommendation serving: ANN retrieval, reranking, ranking systems
- Offline ML/data pipelines using LLMs and embeddings
- LLM-based quality/relevance evaluation systems
- End-to-end ownership from design through production, validated by A/B testing
Common question themes
Design a low-latency personalized recommendation serving system
How would you build an offline pipeline to embed and index content using LLMs
Describe evaluating recommendation quality or relevance with automated methods
Tell me about owning a distributed system component end to end
How do you validate real customer impact of a ranking change (A/B testing)
Related interviews
Amazon
Mid
Software Development Engineer II, Amazon Smart Vehicles
Amazon
Mid
Software Development Engineer, Ads AI Core Infra
Amazon
Mid
Software Engineer II, Leo Regulus
Roblox
New grad
Senior Machine Learning Engineer, Recommendation Systems (PhD Early Career)
Roblox
Senior
Senior Machine Learning Engineer, Ads
Senior