Machine Learning Research Scientist

Company: Oben Bouwmachines
Location: Pasadena , California, United States
Type: Full-time
Posted: 02.NOV.2018
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Summary

ObEN's mission is to enable everyone in the world to create their own Personal AI (PAI), intelligent 3D avatars that look, sound, and behave...

Description

ObEN's mission is to enable everyone in the world to create their own Personal AI (PAI), intelligent 3D avatars that look, sound, and behave like the individual user. Secured and authenticated on the Project PAI blockchain, ObEN's technology creates more productive, more personalized digital interactions. ObEN is a K11, Tencent, Softbank Ventures Korea and HTC Vive X portfolio company, and we work with our strategic investors to expand PAI technology across multiple verticals including hospitality, retail, healthcare, and entertainment.

Working at ObEN means taking on extraordinary transformations every day, in an environment that celebrates and encourages innovation. You'll be working in small, agile teams (including world class researchers in areas of machine learning, NLP, computer vision, speech, and blockchain). We are blazing new trails in AI and blockchain technology, and we encourage and support publications to top conferences and journals. Learn more about working at ObEN in our blog post.

The ML Research Scientist will be responsible for developing novel ML models for projects in the company, like a new TTS model, implementing and improving proposed/existing ML models, and supervising and providing feedback to ML engineers for projects using ML, including those in other teams.

You must have:

  • PhD in Computer Science, Statistics, Electrical Engineering, Applied Mathematics, or related field
  • Strong experience in writing and deploying new algorithms and models
  • Strong research experience in ML, demonstrated by publications in top ML conferences (NIPS, ICML, ICLR, KDD, so on).
  • Hands-on experience in Deep Learning, Generative Models, Transfer and Multi-Task Learning, Structured Prediction, Unsupervised Learning, Graphical Models, Convex Optimization, Active Learning Reinforcement Learning, Bayesian Nonparametric Methods and Semi-Supervised Learning preferred.
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