Build the LLM training/inference infrastructure behind Netflix's global dubbing, subtitling, and localized UI

Netflix's Globalization Data Science and Engineering team is hiring an ML engineer to make LLM and multimodal-LLM training and inference faster, more scalable, and more reliable across Netflix's global catalog. This is a deep systems-and-ML-infra role, not a modeling role - expect questions on distributed training, inference serving, and technical leadership.

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What this interview tests

Distributed training (parallelism strategies, mixed precision, high-throughput data pipelines)
LLM/multimodal-LLM inference optimization (KV cache, batching, quantization, long-context serving)
Production ML systems engineering (PyTorch, testing, observability, performance profiling)
Technical leadership: driving cross-functional ML roadmaps with scientists, PMs, and engineers
Mentoring engineers/scientists on large-scale ML systems

Common question themes

Walk through optimizing a training pipeline that was data- or compute-bound - what did you change and what was the measured impact?

How would you design KV cache and batching strategy for a high-throughput, low-latency LLM serving system?

Trade-offs between quantization/model compression techniques and quality for a production media ML model

How do you scale distributed training across many accelerators - what parallelism strategy and why?

Tell me about a time you had to align scientists, PMs, and engineers around a technical roadmap under ambiguity

How candidates describe it

Real Machine Learning Engineer 5 interview stories — retold from candidates' public write-ups, with sources.

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