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Netflix is one of the world's leading entertainment services, with over 300 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. Over 108 million people tuned in to watch the fight between Jake Paul and Mike Tyson, and more than 65 million people watched the NFL games live on Christmas Day in 2024.

At Netflix, our live events create cultural moments, and we have very exciting Live events lined up for the next few years. To support Netflix's vision for Live, the streaming algorithms team is looking for research partners (early-stage PhD students and their advisors) to evolve our systems so that we can support more concurrent live streams with even better quality. If you are an early-stage PhD student who is looking for interesting research problems that have real-world impact, read on to learn more.

Who We Are The streaming algorithms team is responsible for the quality of Netflix playback across Live and VoD. We design adaptive streaming algorithms in Netflix players and optimize system performance across different components on the streaming path. This team influences of the Internet traffic, shapes the Netflix playback experience, and brings studio-quality audio to Stranger Things.

Our current focus is on making our Live streaming system more scalable, with improved quality and lower latency. For this particular internship, we are seeking an early-stage PhD student with a background in computer networking who is motivated to learn more about Live streaming technologies and is interested in interdisciplinary research that combines streaming technologies, computer networking, and Encoding Technologies. We obsess over great playback experiences.

We want playback to start immediately, at great quality, and we never want playback to stop unexpectedly. To provide the best possible streaming experience, we design algorithms that adapt audio and video qualities to ever-changing network conditions, encoding profiles, UI features, and device capabilities. Our team comes from diverse backgrounds, has a balanced gender ratio, and has a wide range of expertise in computer networking, video streaming, data analysis, embedded systems, and software engineering.

Beyond the subscription-based video-on-demand service, we help Netflix expand into Live Streaming and Advertisements. We work highly cross-functionally and jointly develop our algorithms with Netflix CDN, Encoding Technologies, Data Science & Engineering, Consumer Insights, and UI Engineering. Who You Are We are seeking an early-stage PhD student (preferably with their advisor's involvement as well) to assist us in further enhancing the scalability of our Live streaming system and the streaming quality of experience (QoE) on millions of Netflix devices.

You will also have the opportunity to help evolve the system architecture across the entire streaming path and conduct controlled field experiments to validate improvements. More about you: Currently an early-stage PhD student - An early-stage student is defined as a student who is still looking for their thesis topics. We can also accept later-stage PhD students if their thesis is closely aligned with the topic of Live QoE and scalability.

Experience with research projects in one of the following research areas: network measurement, algorithms related to network performance (e.g., congestion control, adaptive streaming), content delivery, or encoding technology. Exhibit a bias to action, avoid analysis paralysis, and are willing to take smart risks Experience with applying statistics and experimentation in research projects is a big plus. Experience with video is a big plus Duration 40 hours per week for a 12-week period between May and September, with the possibility of extending into a longer-term research collaboration

The Summer Internship At Netflix, we offer a personalized experience for interns, and our aim is to offer an experience that mimics what it is like to actually work here. We match qualified interns with projects and groups based on interests and skill sets, and fully embed interns within those groups for the summer. Netflix is a unique place to work and we live by our values, so it's worth learning more about our culture.

Internships are typically 12 weeks long. Conditions permitting, our summer internships will be located in our Los Gatos, CA office. Netflix is an equal opportunity employer that celebrates diversity, recognizing that diversity of thought and background builds stronger teams.

We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. At Netflix, we carefully consider a wide range of compensation factors to determine internship top of market.

We use market indicators to determine compensation and consider your specific internship team, degree, and years of education to get it right. These considerations can cause your compensation to vary and will also be dependent on your location. The overall market range for Netflix Internships is typically $25/hour - $110/hour.

This market range is based on total compensation (vs. only hourly rate), which is in line with our compensation philosophy. Netflix is a unique culture and environment.

Learn more here. Inclusion 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.