Who We Are: The Human in the Loop team is part of Cortex, the central machine learning organization at Twitter. Cortex’s mission is to empower internal teams to efficiently leverage machine learning by providing platform, modeling, and research expertise while advancing the ML technologies within Twitter.
We tackle Twitter-specific challenges by building techniques that leverage the best of machine learning with the best of human expertise. We apply and advance state-of-the-art machine learning techniques to invent new models and systems that can be used to help improve areas across all of Twitter’s business units.
We operate at scale whilst ensuring fair and ethical use of our models and data.
What you'll do: Apply your distributed systems and data engineering expertise to develop services, storage systems, and data pipelines to facilitate state of the art machine learning modeling with applied human insights. You will develop methods to optimize the value of user feedback and human annotation within an active learning framework. You will work closely with interface developers to construct well-designed human annotation tasks. You will architect machine learning pipelines to automate model retraining and deployment. You will implement mechanisms to automatically monitor and assess the quality of machine learning models that are updated in an online fashion.
You will collaborate with product teams to help them apply human-in-the-loop best practices in the context of their applications and use cases. As the authority on human-in-the-loop processes for machine learning, you will be able to influence the team’s roadmap and help product teams seek out new opportunities to leverage our machine learning technology and infrastructure.
You will also be engaging with the research community via publications and conferences. Twitter, and our team, are suitably positioned to be thought leaders in the space of human-in-the-loop machine learning technology for social media content understanding.
Who you are: You are focusing on the heavy software engineering required for building large-scale Machine Learning applications. This includes ML platform engineering, where you are building ML platform products for our users. More specifically, you are doing the following kind of Software Engineering work:
In the role, you are employing a basic understanding of active learning and other machine learning techniques.
You regularly verify the performance & correctness of the implementations of ML techniques. You are able to triage and fix bugs/issues when they arise.
You craft & implement ML platforms/libraries/services to enable customers with applying ML.
You regularly architect systems and data pipelines taking care of a large amount of data, scale, and low latency.
You employ SWE skills in terms of system architecture & design, distributed systems, coding, OO/API, testing.