Engineering Manager, Offline Inference, Machine Learning Platform

Netflix is one of the world's leading entertainment services, with 283 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

Machine Learning powers innovation in all areas of the business, including helping members choose the right title for them through personalization, better understanding our audience and our content slate, creating high-quality subtitles, dubbings, images, trailers, and other assets, optimize our payment processing, and much more. The Machine Learning Platform (MLP) organization builds highly scalable and differentiated ML infrastructure to maximize the business impact of all ML practitioners at Netflix, which is the key to accelerating this innovation.

The Opportunity

The Offline Inference team builds and maintains the infrastructure that enables ML practitioners to run their large-scale offline batch inference workflows to generate and store model predictions. We specialize in handling user inferencing submissions that rely on pre-specified static data inputs and machine learning models, packaging them into prediction jobs that can take anywhere from minutes to multiple days to complete, and storing and serving the results.

We are looking for an experienced ML/AI infrastructure engineering leader to lead the development of our next-generation offline inference platform! You will lead this newly formed team to architect, design, develop, test, and launch a brand-new platform to enable ML practitioners across the content, studio, consumer, ads, and games domains to effortlessly package, deploy, and execute inference workflows for thousands of large-scale models, including Large Language Models (LLMs), computer vision and foundation models. The models will come from various lifecycle stages, including early research and experimentation, development, productization, and ongoing innovation and optimization of productized models. 

We are a highly collaborative team. You will be highly cross-functional in partnering with other engineering, product management, machine learning, and data teams to take Netflix’s ML/AI initiatives to the next level. To succeed in this role, you will need a strong ML infrastructure background and a passion for building scalable, robust systems that enable and accelerate our ability to apply large and complex ML models across various domains. 

In this role, you will:

• Partner with Applied Research and backend application teams to understand their needs and gather and iterate on the platform's requirements. 

• Drive the architecture, design, build vs. buy evaluation, and execution of the offline inference platform. Strive for extensibility and ensure the platform can scale to meet the needs of the evolving ML/AI landscape.

• Build a roadmap focused on incremental delivery. Define success metrics, align on migration goals, and drive the platform's adoption. 

• Communicate progress to stakeholders, customers, and senior leadership. 

• Maintain existing platform offerings, balancing immediate needs in current systems while prioritizing the development of the next-gen platform. 

• Hire and grow diverse, highly talented engineers while maintaining and fostering an inclusive team culture.

To succeed in this role, you will need:

• 10+ years of software engineering experience and 3+ years of management experience. 

• Experience leading teams responsible for building high-traffic distributed systems and ML infrastructure

• Experience with containerization and orchestration technologies to support data preparation, processing, and inference for large-scale ML models.

• A proven understanding of ML frameworks and commercial ML/AI infrastructure, such as PyTorch, SageMaker, Ray Serve, and HuggingFace.

• Strong technical acumen and can act as a credible technical advisor to the team, set and enforce a high-quality bar for code and system design, and be a mentor for the team.

• A passion for translating the needs of ML practitioners into infrastructure offerings with an eye toward automated and self-serve capabilities.

• Strong communication and collaboration skills and ability to build strong relationships with internal customers and external partners. 

• A demonstrated ability to develop, drive, and execute a technical vision and roadmap.

• A track record of attracting top talent and leading and growing high-performance, diverse, and highly talented tenured engineers deep into their careers to maximize their impact and deliver results in a fast-paced and dynamic environment.

• Experience managing a hybrid team with partners and team members distributed across (US) geographies & time zones.

To learn more about our ML Platform, you can review the relevant talks/blog posts on the .

At Netflix, we carefully consider various compensation factors to determine your personal top of market. We rely on market indicators to determine compensation and consider your specific job, skills, and experience to get it right. These considerations can cause your compensation to vary and will also depend on your location. 

The overall market range for roles in this area of Netflix is typically $190,000 - $920,000. 

This market range is based on total compensation (vs. only base salary), which is in line with our compensation philosophy. Netflix has a unique culture and environment. Learn more . 

is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.