*Note, we are no longer accepting applications for the summer 2018 Machine Learning Intern position. Keep checking back when applications open for Summer 2019*
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 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.
- 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.
- 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