HyperCube is pioneering a deep learning platform for information retrieval. We provide customers with deep learning search, recommendation, and personalization capabilities that until now have been in the hands of a few large tech giants. By pushing the boundaries of science and technology, HyperCube transforms collections and models into a powerful serving engine that is delightful to use, at any scale.
If you’re excited about hard problems at the intersection of AI, machine learning, search engines, and databases, we would love to meet you! We are looking for collaborative scientists who move nimbly between research and production and dare to make an impact. Our team includes founders of multiple startups, CS professors, and world-class scientists and engineers.
As an Applied Scientist, you will design, benchmark and implement high-performance algorithms and data structures. You will train models, generate insights from data, produce state of the art deep learning research, publish at academic and technical conferences, and communicate with tech leads and customers.
You will have:
- Ph.D. in Computer Science, Electrical Engineering, Mathematics, or Statistics
- Proven research track record: publication of peer-reviewed results in AI/ML
- Proven applied science track record: creation of production facing technologies
- Proven expertise in AI, machine learning, algorithms, and/or data structures
You should have experience in:
- At least one deep learning framework, such as Tensorflow, PyTorch, MXNet
- Knowledge in information retrieval models and metrics
- Collaboration experience with scientists and developers