Machine Learning Researcher
D-Wave Systems
Machine Learning Researcher
D-Wave is building hardware with the goal of computing as fast as nature will allow. We're working hard on exploiting quantum resources which are known to offer large computational speedups on some problems. The D-Wave adiabatic quantum computer is able to sample from probability distributions and optimize functions that are computationally intractable using classical hardware. When viewed as a sampler, the native operation of the D-Wave machine is analogous to a sparsely-connected restricted Boltzmann machine. Similar network dynamics have fueled the recent explosion in the field of deep learning, the results of which are presently deployed in products ranging from speech recognition to image search. The D-Wave applications research team is working to exploit the parallels between these architectures to substantially accelerate learning in deep, hierarchical neural networks.
Position:
We are seeking an experienced machine learning researcher to join our research. In this position you will work with a team of machine learning and quantum computation experts to design and implement novel machine learning algorithms that leverage the power of the D-Wave quantum computer. The candidate will design and implement algorithms being developed by the team, investigate their properties, and help fine-tune their design.
This role offers the chance to do inventive, ground-breaking research in an exciting and collaborative research environment. Our Machine Learning Researchers are given the freedom to work within their passions towards our overall goals, and we encourage exploration of new ideas and publication of the findings.
Responsibilities Include:
- Work closely with machine learning experts to design, implement, tune, and optimize machine learning algorithms.
- Design and implement experiments to test the quality of machine learning algorithms.
- Validate promising research concepts with high quality Python, Matlab, or Julia implementations.
- Apply software engineering best practice: software design patterns, version control, unit tests, documentation.
- Work with a diverse group of technical experts to incorporate their expertise into software projects.
Required Qualifications:
- PhD in computer science, statistics, applied mathematics, or physics.
- Experience in statistical methods for machine learning, ideally including probabilistic models, with a focus on Boltzmann machines and MCMC techniques.
- Demonstrated success in the application of machine learning to practical problems.
- Expert coding abilities. Preference given to demonstrated experience using Python, Matlab, or Julia.
- Self-motivated, proactive and flexible with excellent problem solving skills
Burnaby, BC
Full Time