Machine Learning Engineer 5 - Globalization

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Not verified last checked Jun 13, 2026

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Machine Learning Engineer 5 - Globalization USA - Remote At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next. The Globalization Data Science and Engineering team is at the forefront of removing language barriers and providing a stellar member experience to all our members regardless of their language preferences. We are responsible for the translation and cultural adaptation of all aspects of member interaction, including beautiful localized user interfaces, subtitles, and dubbing of award-winning Netflix originals. We are looking for an experienced Machine Learning Engineer with deep expertise in training and inference efficiency for Large Language Models (LLMs), Multimodal LLMs, and other media ML models. In this rare opportunity, you will design and build systems and infrastructure that make LLM training and inference faster, more scalable, and more reliable across a diverse global catalog and workload. You will partner with a talented cross-functional team of scientists, engineers, product managers, and domain experts to deliver business impact through efficient, production-ready ML solutions. Responsibilities Design and build scalable training and inference systems for LLMs, Multimodal LLMs, and other media ML models. Optimize end-to-end training: data pipelines (streaming, sharding, bucketing), distributed training (parallelism strategies), and mixed precision. Optimize inference and serving: KV cache, batching,

Read the full description at explore.jobs.netflix.net. FewerJobs shows a source-linked preview and links to the original posting.