Staff Machine Learning Research Developer

About the role

D-Wave is seeking a Staff Machine Learning Research Developer to work alongside our researchers, solutions architects, and software developers specializing in various domains (e.g., combinatorial optimization, graph theory, and quantum physics).

You will lead the architectural design and development of our software to enable researchers and solutions architects to rapidly prototype and experiment with quantum machine learning methods. In parallel, you will research and develop machine learning methods exploiting the optimization, sampling, and quantum simulation capabilities of quantum computers.

We are looking for intrinsically motivated individuals who want to make technological and tangible impacts at the intersection of quantum computing and machine learning.

What You'll Do

• Help the team align on best practices for machine learning systems and infrastructures
• Design and develop software for machine learning methods using annealing quantum computers
• Research and develop machine learning methods exploiting optimization, sampling, and quantum simulation capabilities of annealing quantum computers
• Communicate with leadership to identify quantum machine learning opportunities
• Clearly and effectively communicate research findings and insights to other D-Wave teams
• Influence and guide the quantum machine learning roadmap by providing technical feedback to leadership
• Lead and deliver goals on the quantum machine learning roadmap
• Quickly digest research papers, reproduce results, and prototype and develop novel quantum machine learning methods

What You'll Bring

• 6+ years of professional experience in developing deep learning models
• An advanced degree (MS/PhD) in a STEM field, or added years of deep industry experience
• Algorithmic reasoning should be second nature (e.g., data structures and computational complexity)
• Ability to quickly digest research papers and implement methods
• A breadth of knowledge in generative machine learning paradigms (e.g., energy-based models, flow-based models, autoregressive models) complemented by a depth of knowledge in several subdomains
• Strong problem-solving, communication, and collaboration skills

Nice to have

• Familiarity with Monte Carlo methods (e.g., Metropolis-Hastings, Gibbs, parallel tempering and sequential Monte Carlo)
• A solid understanding of Boltzmann Machines (i.e., Ising models, Markov random fields, exponential family distributions)
• Familiarity with probabilistic graphical models
• Familiarity with annealing and gate-based quantum computers
• Expertise with C++ or other low-level programming languages
• Contributions to open-source software
• Familiarity with MLOps ecosystems (e.g., Kubeflow, VertexAI, Airflow)
• Experience in delivering end-to-end software projects---from architect to deployment
• Expertise in building extensible APIs and frameworks around PyTorch (or, e.g., JAX and TensorFlow)