Machine Learning Developer
San Jose Metro Area, CA
As part of the machine-learning team,
- You will design and implement state-of-the-art machine learning algorithms for radar-based interactive sensors.
- You will design, develop, debug, and evaluate custom machine-learning algorithms that will be applied across a range of products and use cases.
- You will work closely with the team to improve algorithm performance and play an integral role enabling new state-of-the-art touchless interfaces.
3 Key Responsibilities
This role will involve the following:
- Design, develop, and evaluate novel machine learning algorithms for a custom radar sensor.
- Train, debug, fine tune, and optimize machine learning algorithms to achieve the ultimate user experience.
- Develop tools to help others train, test, and apply machine learning algorithms to a range of products and applications. Requirements
- Master's or Ph.D. degree in Machine Learning, Computer Science or related technical discipline.
- 3+ years experience in machine learning research for production systems.
- Excellent Python/C/C++ programming skills.
- Experience with a wide range of machine learning techniques, including at least one of the following: convolutional neural networks, generative adversarial networks, recurrent neural networks (LSTM, GRUs, etc.).
- Solid foundation in computer science, with strong competencies in data structures, algorithms and software design.
Nice to have's:
- Experience developing novel machine learning architectures for at least one of the following: radar, LIDAR, audio, video, image recognition, inertial sensors (accelerometers or gyroscopes), other related sensors.
- Experience with Tensorflow or related machine learning libraries.
- Track history of publications in leading machine learning and/or HCI journals.
- Experience developing machine learning algorithms for interactive applications.
- Desire to work in a creative flexible atmosphere on the edge of research and production.
- Ability to communicate technical concepts clearly and effectively.