Quantum Algorithm Engineer – Machine Learning and Quantum Control

About Us 

Quantum Brilliance is the world leader in room-temperature, miniaturised and ruggedised quantum technologies that can be deployed anywhere, and close to where you need it most.  Central to our core offering is the integrated diamond quantum chip, which leverages the unique properties of NV centers in diamonds to be used for both quantum sensing and quantum computing applications, from data centres through to on-premises and edge applications such as autonomous vehicles, robotics, hospitals and aerospace. 

Founded in 2019 by leading experts in diamond quantum science from the Australian National University, we are a full-stack quantum technology company. Headquartered in Sydney Australia, we have labs located in Australia (Canberra & Melbourne) and Germany (Stuttgart & Freiburg) and recently opened and office here in Tokyo, Japan. We are partnering with universities, cutting edge materials and semiconductor companies, and exploring real world applications with HPCs, research institutes as well as industrial companies such as in automotive, telecommunications, aerospace and healthcare.  We are backed by large sovereign wealth funds from Australia as well as global deep tech VCs who are supporting us through this rapidly emerging market. 

The Mission 

We are looking to hire a Quantum Algorithm Engineer – Machine Learning and Quantum Control to join our Quantum Utility Exploration (QUTE) team in Tokyo, Japan.  As an integral member of the QUTE team, you will help to establish the utility of diamond-based quantum systems. For this, you will help develop both classical and quantum machine-learning algorithms that demonstrate the utility of diamond-based quantum computing, and collaborate with quantum algorithm, hardware, and control specialists. 

Core Responsibilities 

As an integral part of the QUTE team, you will be responsible for 

• Machine-learning-oriented algorithm research for NV-based quantum computing paradigms, such as reservoir and neuromorphic computing on gate-based, analogue, and hybrid quantum computers; 
• Design and optimisation of machine-learning-based quantum control and readout protocols for NV-based quantum computing and sensing;
  
• Development and extension of our quantum software stack towards edge computing and quantum-enhanced sensing applications; 
• Close monitoring of state-of-the-art literature and developments as well as frequent visits to scientific conferences;
• Collaborate with an international team in Australia and Europe.
• Engage with and explain technical information to both technical and non-technical audiences/partners/customers.

About You

We value curiosity, adaptability, and a drive for impact. Ideal candidates will have a strong mix of the following:

• Advanced degree in computer science, quantum information, physics, chemistry, computational biology or a related discipline (Master's or PhD desirable); 
• 3+ years of experience in (optimal) control theory, machine learning, or signal processing; experience with quantum sensing or NV platforms is a plus; 
• Strong coding experience with Python and/or C++; 
• Experience with professional software development tools such as git (CI/CD and cmake desirable); 
• Comfortable working in an international environment across continents.
• Excellent communication skills with the ability to interact effectively with all levels internally as well as with customers and partners.

Working at Quantum Brilliance 

At Quantum Brilliance, you will join a team of experts working to create massive, transformative impact. You will join a team of problem-solvers, who are curious and driven to understand and master new things. We pride ourselves on a collaborative environment, where we learn from the unique expertise that each person brings, and support the growth of each team member. 

• Collaborating with colleagues around the world: We currently have labs in several locations across Australia, Germany and
  collaborate with leading research institutions and quantum technology companies. You will have opportunities to travel between QB locations for knowledge exchange. 

• Continuous learning: You are expected to keep pace with the state of the art in the field. If you are switching fields, you will need to rapidly get up to speed with the literature history, something we will help with but that you are expected to drive. There are further exciting learnings in project management, team leadership, and business operations. 

• Research leadership: As you complete projects and build your understanding of the company and its R&D programs, you will be increasingly expected to provide input to help shape roadmaps and targets for subsequent R&D projects. This can include pitching projects to follow-up on discoveries made during previous projects. 

• Give back: you will have plenty of opportunities to share your knowledge within the team. This can include providing technical leadership and mentoring to other team members, doing projects with brilliant interns, and regular internal technical group meetings.