The Machine Learning Engineer role at Netflix focuses on enhancing training and inference efficiency for Large Language Models (LLMs) and other media ML models. The Globalization Data Science and Engineering team aims to remove language barriers and improve member experiences through innovative solutions. This position involves designing and building scalable systems that optimize model training and inference. The engineer will collaborate with a diverse team to deliver impactful machine learning solutions.

• Extensive experience in ML engineering for large, production-grade systems using LLMs and other media ML models.
• Deep hands-on expertise in training optimization including high-throughput data loading and distributed training.
• Strong experience in inference optimization such as KV cache design and batching for low-latency serving.
• Proficient with PyTorch and solid software engineering fundamentals.
• Proven track record of leading ML initiatives and collaborating with stakeholders.

• Design and build scalable training and inference systems for LLMs and other media ML models.
• Optimize end-to-end training including data pipelines and distributed training strategies.
• Optimize inference and serving through techniques like KV cache and quantization.
• Scale model training and inference into robust systems integrated into Netflix workflows.
• Act as a technical thought leader for training and inference efficiency.

• Employees at Netflix are often offered flexible, people-first benefits—unlimited time away, generous parental leave, global family-forming support, mental-health programs (mindfulness, free counseling/coaching), and health coverage tailored by country. Financially, Netflix pays at personal top-of-market and lets employees choose their mix of cash vs. fully-vested 10-year stock options, alongside donation and volunteer matching. Convenience perks can include trust-based travel/expense policies, relocation support, and “Work, Not Drive” rideshare flexibility.