Machine Learning Intern

This position is on the Science team, the team tasked with creating and refining Affectiva’s technology. We are a group of individuals with backgrounds in machine learning, computer vision, speech processing and affective computing. This position is available in Boston, New York City, and Cairo. 

We are interested in hiring a summer intern with interest, experience and expertise in either: 1) building deep learning based models for predicting emotions from face or speech; or 2) collecting large datasets of affective interactions and automatically annotating the dataset with emotion tags. We are very interested in candidates who have hands-on experience tackling these subproblems; for example, if you have build deep learning models for predicting audio or video based targets; or collected audio-video data either via crowdsourcing tasks or by leveraging the large quantities of user-generated tags (e.g., hashtags) available on the public web; or used machine learning based approaches for automatic data annotation, such as bootstrapping labels from one channel to another parallel channel; autonomous learning, collaborative learning, or other innovative semi-supervised and unsupervised approaches.

The candidate will work closely with members of the Science team, the team tasked with creating and refining Affectiva’s technology. The Science team is a group of individuals with backgrounds in machine learning, computer vision, speech processing and affective computing. The Science team does everything from initial prototyping of state-of-the art algorithms to producing models which can be included in our cloud and mobile products.

Responsibilities:
  • Running a multitude of data modeling experiments related to audio or video based emotion classification.
  • Running experiments to perform data annotation experiments related to
    • Bootstrapping labels from video to audio channel and vise versa
    • Autonomous learning paired with collaborative learning based approaches
    • Explore other weakly supervised or unsupervised approaches
  • Design, implement and evaluate crowdsourcing tasks for collecting datasets of affective interactions
  • Clearly communicate your implementations, experiments, and conclusions.

Qualifications:
  • Pursuing undergraduate or graduate degree in Electrical Engineering or Computer Science, with specialization in speech processing or computer vision.
  • Hands-on experience developing methodologies for automatic data acquisition and data annotation problems.
  • Experience using deep learning techniques (CNN, RNN, LSTM), on computer vision tasks or speech processing tasks.
  • Experience working with deep learning frameworks (e.g. TensorFlow, Theano, Caffe) including implementing custom layers
  • Programming Skillset: Python, C++, and other programming and scripting languages
  • Strong publication record in machine learning, speech or computer vision related journals/proceedings
  • Good presentation and communication skills

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