The Software Engineer role at Netflix focuses on building and optimizing machine learning infrastructure for training and serving critical models. The Model Runtime team is a small, autonomous group that directly influences Netflix's AI capabilities. The position requires a blend of systems engineering and machine learning expertise to enhance recommendation models and enable next-generation AI workloads. Candidates will work on scaling distributed training systems and optimizing performance across the technology stack.

• Experience in ML systems engineering, specifically for training and inference of pre-LLM and post-LLM models at scale.
• Strong systems programming skills with the ability to work across multiple layers of the stack.
• Hands-on experience with PyTorch internals and large-scale distributed training.
• Comfortable with ambiguity and capable of executing on diverse projects.
• Experience with cloud computing providers, preferably AWS.
• Excellent written and verbal communication skills.

• Build alignment and post-training infrastructure for reinforcement learning and preference optimization.
• Create infrastructure for multimodal and diffusion models, including distributed training and real-time inference.
• Engineer fault-tolerant training systems using advanced parallelism strategies across GPU clusters.
• Profile and tune performance from high-level ML frameworks down to GPU kernels.
• Evaluate emerging hardware and frameworks to maintain efficiency in ML operations.

• 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.