Software Engineer 5 – Training Platform, AI Platform

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.

Netflix is the world's leading streaming entertainment service, with 278 million paid members in over 190 countries, enjoying TV series, feature films, and games across numerous genres and languages. Members can watch or play as much as they want, anytime, anywhere, on any internet-connected screen. 

Machine Learning/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right title for them through personalization, to better understanding our audience and our content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.

The Opportunity

We are looking for a senior engineer with deep expertise in distributed model training and the systems required to operate it at scale. You will help shape the architecture of our training platform, improve performance and reliability for large training jobs, and build intuitive platform experiences for ML engineers. Our infrastructure is built on Kubernetes, Ray clusters, and PyTorch distributed training primitives.

This role requires strong technical depth, sound engineering judgment, and the ability to influence across teams. You will partner closely with ML engineers, researchers, infrastructure teams, and platform stakeholders to identify needs, lead technical discussions, drive alignment, and deliver high-impact capabilities.

charter is to maximize the business impact of all AI use cases at Netflix through highly reliable and flexible AI tooling and infrastructure that supports key product functions such as personalized recommendations, studio algorithms, virtual productions, growth intelligence, and content demand modeling among others.

In this role you will get to: 

• Design and build the platform that powers large-scale machine learning model training, fine-tuning, model transformation and evaluations workflows and use cases from the entire company

• Co-design and optimize the systems and models to scale up and increase the cost-effectiveness of machine learning model training

• Design easy-to-use APIs and interfaces for experienced ML practitioners, as well as non-experts to easy access the training platform

Minimum Job Qualifications

• Design, build, and operate platform infrastructure, libraries, and SDKs for large-scale model training. Enable reliable and efficient training workflows for foundation models and generative AI models of all sizes.

• Diagnose and optimize the performance of large distributed training jobs, including GPU utilization, memory efficiency, communication overhead, data loading, checkpointing, fault tolerance, and cluster utilization.

• Experience with cloud computing providers, preferably AWS

• Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects

• Adopt and promote best practices in operations, including observability, logging, reporting, and on-call processes to ensure engineering excellence.

• Excellent written and verbal communication skills

• Comfortable working in a team with peers and partners distributed across (US) geographies & time zones.

Preferred Qualifications

• Understand modern and real-world Machine Learning model development workflows and experience partnering closely with ML modeling engineers. Lead technical design reviews, facilitate cross-functional discussions, communicate tradeoffs clearly, and align stakeholders on platform direction and execution priorities.

• Familiarity with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, etc.)

• Familiarity with distributed training performance analysis tools and techniques, such as PyTorch Profiler, NVIDIA Nsight Systems, GPU telemetry, communication profiling, or cluster-level utilization analysis.

• Experience with large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelism

• Experti