2018/2019 Intern - Cortex (Magic Pony)

London

Who we are:

We are a community of Machine Learning Researchers and Engineers, working to improve Twitter through applications of ML through a range of systems - e.g. recommendations, safety, abuse, ads. We operate at scale whilst ensuring fair and ethical use of our models and data.

We work as embedded researchers amongst product teams, looking to apply the expertise of the individuals to improve our products and unlock new capabilities.

What you will do:

You will apply your research experience to problem spaces related to our ML-driven products. You will work with us to develop a research proposal, perform literature review, implement prototypes and document your analyses. You will work closely with researchers and research engineers in our team.

Who you are:

You are working towards PhD in a ML-driven field - e.g. NLP, Computer Vision, Prediction/Inference, etc and you are interested in applying your knowledge and skill set to one or more of our product areas - e.g. media / content understanding, behavioural understanding, recommendation systems, model performance optimisation. You are passionate about the way we develop 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.

Requirements:

  • PhD student in a machine learning related field
  • Good theoretical grounding in core machine learning concepts and techniques
  • Ability to perform literature reviews and provide critical feedback
  • Familiarity with one or more DL software frameworks such as Tensorflow, PyTorch

Nice to haves:

  • Publications in top conferences such as ICLR, NIPS, ICML, CVPR, ICCV, ECCV, etc
  • Experience with one or more of the following:
    • Approximate / k-nearest neighbour theory, algorithms and frameworks
    • Natural Language Processing
    • Recommender Systems
    • Model optimisation
    • Prediction / Inference (e.g. Bayesian)
    • Online Learning
    • Reinforcement Learning


Engineering Hiring Process

Step 1

Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.

Step 2

If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.

Step 3

If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.

Application

By applying you expressly make the following representations and warranties and give your consents as described below:
Twitter, Inc. collects your personal data for the purposes of managing Twitter, Inc.’s recruitment related activities as well as for organizational planning purposes globally. Consequently, Twitter, Inc. may use your personal data in relation to the evaluation and selection of applicants including for example setting up and conducting interviews and tests, evaluating and assessing the results thereto and as is otherwise needed in the recruitment processes including the final recruitment. 
Twitter, Inc. does not disclose your personal data to unauthorized third parties. However, as a global corporation consisting of multiple affiliated companies in various countries, Twitter, Inc. has international sites and Twitter, Inc. uses resources located throughout the world. Twitter, Inc. may from time to time also use third parties to act on Twitter, Inc.’s behalf. You agree to the fact that to the extent necessary your personal data may be transferred and/or disclosed to any company within Twitter, Inc. group of companies as well as to third parties acting on Twitter, Inc.’s behalf, including also transfers to servers and databases outside the country where you provided Twitter, Inc. with your personal data. Such transfers may include for example transfers and/or disclosures outside the European Economic Area and in the United States of America.

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