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Machine Learning Software Engineer:

Machine Learning Research Scientist/Engineer:

Join the Netflix Algorithms Engineering team! We’re looking for great machine learning applied researchers and ML software engineers to help shape the future of entertainment, spanning personalization for recommendations, search, messaging, and growth. Links below.

We're now accepting applications for Machine Learning research internships at Netflix Research for summer 2024, including in our personalization, recommendations, and search teams. Find out more and apply here:

The slides from our #RecSys2023 talk on "Reward Innovation for long-term member satisfaction" are now available here:

Miss the #RecSys2023 IERI workshop talk on "Recommendation Modeling with Impression Data at Netflix" from @panjiangwei? Don't worry, the slides are now available here:

Also for those attending #RecSys2023 in-person, come hear @panjiangwei give a presentation on the work during Session 17 starting at 4:05 today.

A good reward function is critical for your recommender, bandit, or RL model to perform well. Check out our #RecSys2023 industry track paper on how we built a system to make it easier to test new reward definitions for our recommendation models.

For those attending #RecSys2023 today in-person, its also being presented as a poster by Ding Tong. Drop by if you want to learn more.

Feedback loops in Recommender Systems aren't mythical like bigfoot; they're real. Check out our #RecSys2023 industry track paper on lessons learned in detecting and addressing them at Netflix.

Learn about using RL to optimize recommendation pipelines pipelines in the #RecSys2023 paper by Kabir Nagrecha. It will be presented in session 10 at 2PM today. Paper here:

Join our Mark (Ko-Jen) Hsiao at the #RecSys2023 VideoRecSys workshop for a presentation on "From Stranger Things to Your Favorite Things: Netflix's Recommendation Evolution". The talk will be at 4:35 today in room 327.

For those attending #RecSys2023, come see a talk from our @panjiangwei on "Recommendation Modeling with Impression Data at Netflix" at the LERI workshop at 2:00.

RT @ZhankuiHe: 🤖️ Are LLMs good Conversational Recommender Systems (CRS) ? .We (@McAuleyLabUCSD and @NetflixResearch) let LLMs generate mov….

Next in our series of Media ML blog posts, we talk about a couple of approaches that we developed at Netflix to algorithmically infer scene changes and boundaries, using video and audio features:

Don’t miss the “Practical Bandits - An Industry Perspective” tutorial from #TheWebConf2023, featuring insights from our very own Ying Li and Devesh Parekh. Check out the materials here:

Special call out to the accompanying video:

Our third post in the series about how Netflix uses Machine Learning and Computer Vision to make better media is up. We go deep on causal impact of successful visual components of promotional artwork on our member’s choosing experience.

RT @__sudarshan__: I'm organizing a workshop on Machine Learning for Streaming Media at the #webconf2023 along with my colleagues - @pchand….