Netflix · Engineering blog · 5.7 a month · 26 posts since Jun 23, 2026
Oct 26
Serdar Cellat, Yuchen Xie, Bernadette Marcu, Blaid Burgess, Cody Jarrett, Carlos Pereira, Qing Shi, Samuel Carvajal, Manuel Pamplona, Dorothy Jung, Feiko Ritsema, Benjamin Klein, Alex Woo, Eugene Lok, Anna Pulido, Rebecka Swe, Sanford...
Oct 26
By Praneeth Yenugutala, Victor Dusa, Anastasia Samoilova, Megan Ren, Justin Wei, Marcelo Mayworm At Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such...
Oct 23
By Roberto Perez Alcolea, Java Platform Team At Netflix, shared Java libraries support applications developed across a large, independently evolving repository ecosystem.
Oct 22
Samartha Chandrashekar & Nick Parker You click play on a Friday night, and in the second that follows, dozens of services answer in milliseconds. Most of them run on Titus, our internal container platform.
Oct 16
Prasanna Vijayanathan, Renzo Sanchez-Silva When Jake Paul and Mike Tyson stepped into the ring in November 2024, we delivered the fight to roughly 65 million concurrent streams.
Oct 14
How we detect failing GPUs and network fabric, remediate machines automatically, and let long-running jobs survive hardware loss.
Oct 14
By Violetta Pidvolotska and Naveen Mareddy Stratum is Netflix’s internal serverless media processing platform, shaped by over a decade of experience developing large-scale distributed systems.
Oct 14
By Emma Yanyang Kong, JJ Tan, Ishan Gupta, Lars Olds, Claire Campbell, David Fagnan, Ratna Kavuri, Rohan Gosain, Veli Balin, Minsu Jang, Louis Garcia Introduction Any team generating text with an LLM at scale hits the same problem.
Oct 14
By Prashanth Gedde Narayanaswamy As one of Netflix’s largest and critical data pipelines, member-sessionizer consolidates tens of billions of hourly client events, encompassing user taps, playbacks, and engagement signals, into individual...
Oct 14
Netflix Conductor: The Next Chapter by Aravindan Ramkumar on behalf of the Conductor team Conductor is the workflow orchestration engine Netflix uses to stitch micro-services into reliable, observable business processes.
Oct 14
By Yogesh Nagarur Introduction In Part 1 of this series, we shared how Netflix built the Ads Event pipeline and approached it as a system design problem: replacing fragmented pipelines with a centralized collection, enrichment, and a...
Oct 14
By Oleksii Tkachuk, Rajiv Shringi, Chris Lohfink, Abe Ratnofsky, Kartik Sathyanarayanan Introduction In an earlier post, we introduced the TimeSeries Abstraction, our high-throughput, low-latency platform designed to store and query...
Oct 14
By Kevin Zielnicki, Guy Aridor, Aurélien Bibaut, Allen Tran, Winston Chou, and Nathan Kallus Introduction Personalization is everywhere on the internet.
Oct 14
by Olek Gorajek, Samuel Redai, Yian Shang Intro Our Netflix journey from data to dashboards, particularly within the space of the experimentation platform, always presented a unique set of challenges.
Oct 14
Authors: Divya Gadde, Ko-Jen Hsiao, Dhaval Patel and Moumita Bhattacharya The motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post.
Sep 25
By Dhruv Pratap Introduction Organizations that have been around for a while usually run two identity systems side by side. One belongs to the cloud provider: IAM roles, instance profiles, execution roles.
Sep 18
Introducing composable, module system native and agent friendly command line tools for modern Java development By Danny Thomas, JVM Ecosystem Team Recent work on the Java language to pave the on-ramp has made it easier than ever to start a...
Aug 28
By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray Zhang Introduction The Netflix experience is a journey of discovery.
Aug 21
Samuel Yeboah, Francesco Di Chiara and Mingliang Liu Today, Netflix runs two Flink autoscalers. That is exactly one more than we want. We built the first one in-house years ago, when there was no mature option suited to our platform.
Aug 7
How and Why Netflix Built a Real-Time Distributed Graph: Part 3, Querying the graph with gRPC execution API Authors: Nilesh Mishra and Ajit Koti This is the third entry of a multi-part blog series describing how we built a Real-Time...
Jul 31
by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh Selveraj Netflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a...
Jul 30
Authors: Ying Li, Arjun Rao, Shradha Sehgal Introduction Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with...
Jul 17
By AI Platform’s Model Runtime team and Inference team Introduction Most organizations consume LLMs through hosted APIs. Netflix went further, we run the full stack ourselves, from model deployment through inference, inside our existing...
Jul 13
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan Fisher A deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed...
Jun 29
Authors: Lequn Wang, J iangwei Pan, and Linas Baltrunas Introduction The Netflix homepage is the first thing users see when they open the app and the primary way they discover content to enjoy.
How Netflix builds streaming, data and ML platforms at scale.
Data engineering (8), Machine learning (4), AI and agents (4) in the last six months.
FeedNov 6
failing to ok
FeedNov 5
ok to failing: answered 504
CadenceOct 15
a few a month to about weekly
CadenceOct 2
about weekly to a few a month
CadenceSep 26
a few a month to about weekly
Official checkSep 25
not reviewed to official: netflixtechblog.com, linked from research.netflix.com
Blogsby Glyph