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Deep 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.

We are interested in hiring an intern with interest, experience and expertise in either: 1) building deep learning based models for predicting emotions from face or speech; or 2) building deep learning infrastructure and data processing pipelines.

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, 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:
Building data fetching and processing  pipeline for multimodal deep learning experiments.
Running a multitude of data modeling experiments related to audio or video based emotion classification.
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
  • Good presentation and communication skills
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