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# Machine Learning Engineer 5 - Globalization

- **Company:** Netflix, Inc.
- **Location:** Philadelphia, PA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Profiling, Extract Transform Load (ETL), Shard (Database Architecture), Distributed Computing Environment, Machine Learning, Pytorch, Large Language Models, Build Management, Low Latency, Machine Learning Operations, Data Pipelines
- **Published:** August 9, 2026
- **Apply:** https://www.workingnomads.com/job/go/1779945/

## About the Role

* Extensive experience in ML engineering for large, production-grade systems using LLMs, Multimodal LLMs, and other media ML models.
 * Deep hands-on expertise in training optimization: high-throughput data loading (streaming, sharding, bucketing); distributed training (parallelism strategies); GPU/accelerator optimization.
 * Strong experience in inference optimization: KV cache design and optimization; batching and scheduling for high-throughput, low-latency serving; quantization and/or model compression.
 * Proficient with PyTorch and solid software engineering fundamentals (testing, observability, performance profiling).
 * Proven track record of leading ML initiatives and partnering with stakeholders to define and execute impactful roadmaps.
 * Exceptional communication and collaboration skills; comfortable with ambiguity and high ownership.
 * Netflix culture resonates with you.

## Description

* 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, quantization, and long-context handling.
 * Scale model training and inference into robust, performant systems integrated into Netflix workflows.
 * Act as a technical thought leader for training and inference efficiency, driving initiatives that significantly improve scalability, latency, and reliability.
 * Mentor and uplevel other engineers and scientists in large-scale ML systems and performance engineering.


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