RADAR · Anywhere · Sept. 17, 2018
💰 $110,000 - $170,000 / yr
We’ve built tech that takes RFID a giant leap forward by truly fusing it with Computer Vision. Faster RFID reads and better location accuracy means we know where things are and when they move even before overlaying CV. As an Applied Research Scientist focused on Machine Learning/Intelligence you will be responsible for leading our research initiatives to apply ML/DL techniques to our retail applications and beyond.
- Work directly with the technical CEO and CTO to prototype and productize ML based features.
- Design and innovate methods for collecting and annotating training data.
- Leverage and combine data across different types of sensors to create novel learning techniques and applications.
- Work with backend and systems engineers to build highly distributed and fault tolerant ML / CV infrastructure and pipelines.
- Write and maintain high quality, stable, and easily testable code.
- Experience in Python including libraries such as NumPy, TensorFlow, and SciKit
- Experience across a wide variety of deep learning techniques such as supervised learning, one-shot learning, reinforcement learning, synthetic gradients, and GANs
- Experience training and applying CNNs with various types of datasets
- Experience utilizing the GPU using CUDA or OpenCL.
- Background in Computer Vision or Machine Learning
- Experience with Java or C++
- Desire to work with intelligent, ambitious, and humble individuals in a fast-paced, invention-driven, startup environment
- An understanding of wireless systems preferred
WHAT IT’S LIKE TO WORK WITH US:
We’re passionate about the technology we’ve created and what we’re building, but we know that changing any industry and creating a successful company will take balance, maturity, and a sustained effort. We’ve combined retail industry expertise, amazing engineers with experience shipping real-world hardware solutions, and a team of brilliant minds who are not afraid to focus on solving “impossible” problems. But this passion doesn’t mean we live unbalanced lives. We have families and passions outside of work, and we know that the best work comes from sharp, rested people. We respect each other and each of our contributions, and we believe that the best solutions will come from a diversity of ideas and perspectives.
Finally, we build our products with deep empathy for the people who will use them every day. Their input and insights are our clearest guide to building what they need; we respect our partners and clients, and listen closely to their feedback.