Senior Machine Learning Engineer, Humans in the Loop (Cortex)

The Position

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 will do: 

Apply your machine learning expertise to develop algorithms, modeling techniques, and tools to streamline human-in-the-loop model training processes. 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 systems.

 

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 have sound knowledge of state-of-the-art machine learning models (in particular deep learning models for NLP, computer vision or other areas relevant to social media platforms) and are capable of applying them to real-world problems. You understand best-practices for applying data collection and annotation techniques for training machine learning models. You are comfortable with building production-grade software systems, and are up-to-date with software engineering best practices. You are keen to continue learning and developing your expertise in machine learning.

Qualifications

  • Masters degree or Ph.D. in Computer Science or Machine Learning related degree; or equivalent work experience in the field.

  • 3+ years applied research experience, preferably applying machine learning to real-world problems in the industry.

  • 2+ years of experience building production machine learning models, and deploying them to solve inference challenges at scale.

  • Experience with data collection, data annotation, and active learning.

  • Solid theoretical grounding in core machine learning concepts and techniques.

  • Ability to perform comprehensive literature reviews and provide critical feedback on state-of-the-art solutions and how they may fit different operating constraints.

  • Familiarity with one or more deep learning software frameworks such as Tensorflow, PyTorch.

  • Preferably publications in top conferences/journals such as NeurIPs, ICML, ACL, ICCV, etc.

Company Description

Twitter is what’s happening and what people are talking about right now. For us, life's not about a job, it's about purpose. We believe real change starts with conversation. Here, your voice matters. Come as you are and together we'll do what's right (not what's easy) to serve the public conversation.

 

Team

Machine Learning, Software Engineering

Location

Toronto, Remote Canada, Boston

 

Application

U.S. Equal Employment Opportunity information (Completion is voluntary)
Non U.S. Equal Employment Opportunity information (Completion is voluntary)
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