Who we are:
We are a community of Machine Learning Researchers and Engineers, working to help Twitter leverage ML through a range of systems such as recommendations, safety, abuse, content understanding, ads and more. We operate at scale whilst ensuring fair and ethical use of our models and data.
We work collaboratively, often embedding among product teams, looking to apply the expertise of the individuals to improve our products and unlock new capabilities. We encourage publishing papers, but they are not the end goal, rather a by-product of us doing interesting work - the aim is to make a real-world impact!
The Recommender Systems Research Team, part of Cortex Applied Research, both performs fundamental recommender systems research and builds some of our most sophisticated recommender systems for the most important products at Twitter. We strive to understand the social network via state of the art ML and a deep understanding of our domain, and to recommend content that sparks joy. In this team, you'll be working closely with leading researchers and will have the opportunity to learn about the current state-of-the-art in ML, contributing to ambitious research projects based on the latest insights.
What you will do:
Apply your research and engineering skills to bring ML research ideas to production. You will work closely with your fellow researchers and engineers to scale up models for both training and inference on state-of-the-art hardware. You will contribute to the design of new systems and infrastructure to shape how ML is used across the business. Apply your wider experiences to provide perspective, and enable Twitter to benefit more rapidly from fundamental ML research. In addition, you will be directly contributing to research projects, strategic decisions and future roadmaps for products and technologies at Twitter. You will work on the handful of most important models at Twitter.
Who you are:
You are excited about the challenges of deploying the latest ML technologies at scale and enabling research to go from idea to practical impact. You have a deep understanding of the interplay between ML techniques and the hardware compute constraints. You’re a diligent listener, have the ability to communicate technical issues to non-experts, and can persuade others to follow your recommendations. You have a solid understanding of core ideas in machine learning and software engineering, keep up-to-date with the latest developments in the field and look for ways to apply them in the real world. You are deeply pragmatic, and know when to build for the long-term, and when to do quick experimentation to bring clarity in the short-term.