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Who we are:
The Natural Language Processing (NLP) research 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.
Our team aims to improve Twitter’s product by unlocking new capabilities in the NLP domain and tackle Twitter specific challenges such as the real-time, ever changing nature of our data and limited context. We encourage publishing papers though they are not the end goal. Publication are rather a by-product of us doing interesting work as the end goal is to make real-world impact. We operate at scale whilst ensuring fair and ethical use of our models and data.
What you will do:
Apply your NLP research expertise to improve our ML-driven products, help us develop novel solutions, and unlock new directions. You’ll collaborate with product teams, mentor them on best practices for modern NLP, and keep the wider team informed on the state of the art. In addition, you will be in a strategic position to influence future roadmaps for NLP-driven products. You will engage with the research community via publications, workshops, and tutorials.
Who you are:
You have an in-depth knowledge and research experience in NLP. You are passionate about state-of-the-art technologies and are excited by the application of theory to real-world problems. You keep up to date with the latest developments in the field and look for ways to apply them to your current work/role.
- Post-graduate or PhD in Computer Science or Machine Learning related degree with a focus on NLP; or equivalent work experience in the field
- 3+ years NLP research experience
- Good 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 to different operating constraints
- Experience with a number of ML techniques and frameworks, e.g., data discretization, normalization, sampling, linear regression, decision trees, SVMs, deep neural networks, etc.
- Familiarity with one or more DL software frameworks such as Tensorflow, PyTorch
- Publications in top conferences/journals such as EMNLP, ACL, COLING, TACL, CoNLL, NeurIPs, ICML.
Nice to haves:
- Experience with large-scale systems and data, e.g. Hadoop, distributed systems
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