Netflix is seeking an experienced Machine Learning Engineer to enhance training and inference efficiency for Large Language Models and other media ML models. The role involves designing scalable systems and collaborating with a cross-functional team to deliver impactful ML solutions.

• 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, distributed training, GPU/accelerator optimization.
• Strong experience in inference optimization: KV cache design, batching, scheduling, quantization, and model compression.
• Proficient with PyTorch and solid software engineering fundamentals.
• Proven track record of leading ML initiatives and partnering with stakeholders.
• Exceptional communication and collaboration skills.

• Design and build scalable training and inference systems for LLMs and other media ML models.
• Optimize end-to-end training: data pipelines, distributed training, 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.
• Mentor and uplevel other engineers and scientists in large-scale ML systems.

• 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.