Responsibilities
- Develop efficient & scalable data pipelines to harness rapidly growing datasets in multiple sports
- Optimize & deploy novel machine learning models on resource-constrained mobile devices
- Train models to solve complex tasks such as ball tracking, player tracking, automated scoring, etc.
- Create visualization tools for monitoring experiments and analyzing datasets
- Contribute & test new ideas as a part of a continuously improving state-of-the-art application
- Stay up to date on the latest machine learning literature and industry breakthroughs
- Work on a collaborative team and effectively communicate results to non-technical teams
Requirements
- Experience in building and training neural networks in PyTorch or TensorFlow
- Experience in visualizing network training performance with Tensorboard
- Experience with numpy, scipy, matplotlib, scikit-learn, Jupyter notebooks & bash scripting
- Strong engineering practices, debugging skills, and familiarity with multi-threaded programming
- Proficiency in linear algebra, algorithms, machine learning, optimization & numerical methods
- Comfortable with UNIX, git, etc.
- BS/BA in Math, Statistics or Computer Science
Preferred
- Familiarity with CoreML and Swift
- Familiarity with computer vision concepts and OpenCV
- MS or PhD in Math, Statistics or Computer Science
Bonus
- Avid user of the SwingVision app
- Passion for Racket Sports
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