MotionSavvy is bringing 3D sensing technology to the deaf community to provide real time sign language interpretting in a small and effecient form factor. It's product name UNI is designed to run on standard off the shelf hardware such as windows surface pro 4 tablets.
MotionSavvy has been recognized in numerous publications for it's innovative work that impacts roughly 360M+ deaf individuals. Being deaf in today's world is like moving to a country that speaks a different language and never being able to learn the language spoken.
If you strip away all the details of what our system actually is, large parts of the recognition side of things turn out to be doing classification and segmentation on high-dimensional time series data. For that matter, many other problems can be made to look a bit like that too. Techniques developed for working with speech, financial data, sensor telemetry, online stroke-based handwriting recognition and so on may be applicable to the larger catagory of problems, and all of this stuff calls for algorithms and approaches quite a bit different from many other common machine learning tasks. We are looking for a machine learning specialist with a nack for these kinds of problems who can lend their expertise to our team.
- Time Series Analysis
- Develop and improve methods for sign and fingerspelling recognition, primarilly using skeleton tracking data
- Help the machine learning team with other tasks as required
- Substantial experience working on sequence labeling, time series segmentation, approximation, or classification, information retreival with variable length time series data or similar problems
- Proficient with Python
- Familiar with Git or another modern, distributed version control system
- NoSQL database experience is a plus
- Significant research experience is a plus
- ASL proficiency is a plus
- $16 / hour
- 10-20 hours / week