D-Wave (NYSE: QBTS), D-Wave is a leader in the development and delivery of quantum computing systems, software, and services. We are the world’s first commercial supplier of quantum computers, and the only company building both annealing and gate-model quantum computers. Our mission is to help customers realize the value of quantum, today. Our quantum computers — the world’s largest — feature QPUs with sub-second response times and can be deployed on-premises or accessed through our quantum cloud service, which offers 99.9% availability and uptime. More than 100 organizations trust D-Wave with their toughest computational challenges. With over 200 million problems submitted to our quantum systems to date, our customers apply our technology to address use cases spanning optimization, artificial intelligence, research and more. Learn more about realizing the value of quantum computing today and how we’re shaping the quantum-driven industrial and societal advancements of tomorrow: www.dwavequantum.com.
You can read more about our company and our innovations in the pages of The Wall Street Journal, Time Magazine, Fast Company, MIT Technology Review, Forbes, Inc. Magazine, Wired and across many whitepapers.
At D-Wave, we’re helping customers realize the value of quantum computing today and are shaping the quantum-driven industrial and societal advancements of tomorrow.
About the role
We are seeking a highly skilled and innovative Benchmarking Researcher II to join our Algorithms, Performance and Tools team. The Benchmarking Researcher II will be a part of the internal benchmarking team (BMT) at D-Wave. The main task of this role will be to work on algorithmic improvements within the Hybrid Development Program at D-Wave. In this role, you will conduct independent research and communicate your results to our internal teams and stakeholders. To be successful in this role, a strong understanding of algorithms and hands on coding skills in Python will be required. D-Wave Systems believes that quantum computing has the potential to help solve some of the most complex technical, scientific, national defense, and commercial problems that organizations face. Today, we offer a full stack of systems, software, developer tools, and services to enable enterprises, governments, laboratories, and academic institutions to access the power of quantum computing.
What you'll do
- Conduct research and development activities focused on benchmarking and characterization of our quantum computing systems
- Implement algorithmic ideas for improving the operation of our QPU
- Design novel hybrid algorithms
- Conduct experiments to characterize QPU contribution to different hybrid algorithms
- Work to expand and improve software pipelines for managing benchmarking experiments
- Analyze and track results over time to identify promising areas of performance
- Communicate findings internally and externally
- Working with a diverse group of technical experts to incorporate their expertise into software projects
Required
- 3+ years of experience in a full-time research position
- Intermediate-level Python programming skills
- Master's or PhD in Computer Science, Physics, Mathematics or equivalent experience
- Knowledge of machine learning algorithms, Monte Carlo sampling methods, and/or statistical physics
- Ability to clearly understand, articulate and implement algorithms based on scientific research papers
- Experience conducting independent research
- Experience with exploratory and graphical methods of data analysis
- Proficiency in technical writing
- Demonstrated experience in algorithm development
A D-Waver's DNA
- We look at the future and say “why not”; we see possibilities where others see problems or routines. We show the way ahead and are committed to achieving ambitious goals.
- We practice straight talk and listen generously to each other with empathy. We value different opinions and points of views. We ensure that we connect outside as well as inside to learn from others and inspire each other.
- We hold ourselves accountable for delivering results. We make decisions & take responsibility so that we can act & support each other.
- As leaders we motivate & engage our teams to undertake beyond what they originally thought possible, by developing our teams & creating the conditions for people to grow and empower themselves through enabling & coaching.
Our Compensation Philosophy is Simple but Powerful:
We believe providing D-Wavers with company ownership, competitive pay, and a range of meaningful benefits is the start of creating a culture where people want to give the best they’ve got — not because they’re simply making money, but because they’ve fallen in love with our vision, mission, values, and team.
During the interview process, your Recruiter will review our total rewards (base, equity, bonus, perks, benefit, culture) offerings. The final offer is determined by your proficiencies within this level.
Inclusion:
We celebrate diverse perspectives to drive innovation in our pursuit. Our employees range from distinguished domain experts with decades of experience in their respective fields, to bright and motivated graduates eager to make their mark. Our diverse and innovative team will make you feel appreciated, supported and empower your career growth at D-Wave.
The Fine Print:
No 3rd party candidates will be accepted
It is D-Wave Systems Inc. policy to provide equal employment opportunity (EEO) to all persons regardless of race, color, religion, sex, national origin, age, sexual orientation, gender identity, genetic information, physical or mental disability, protected veteran status, or any other characteristic protected by federal, state/provincial, local law.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of Benchmarking Researchers represents a critical transition in the quantum sector from experimental verification to industrial performance standards. As the ecosystem moves beyond lab-scale demonstrations, the structural necessity for roles that quantify algorithmic efficiency and hardware fidelity becomes paramount for commercial viability. This function serves as a high-leverage point within the software-hardware interface, ensuring that hybrid quantum-classical workflows meet the rigorous stability requirements of enterprise-grade high-performance computing (HPC) environments. By bridging the gap between theoretical algorithmic research and deterministic performance metrics, this role type facilitates the objective assessment of Technology Readiness Levels (TRLs). Market signals indicate that such expertise is essential for mitigating the risks associated with the current lack of standardized benchmarking protocols across fragmented hardware modalities. This function ultimately secures the foundation for reproducible quantum advantage within the global deep-tech value chain.
The quantum computing landscape is undergoing a decisive shift toward the integration of high-fidelity computational kernels within global enterprise ecosystems. While hardware development continues to scale the number of qubits, the primary bottleneck for industrial adoption has shifted to the algorithmic layer, specifically regarding the reproducibility and interoperability of hybrid workflows. Current industry focus lies on bridging classical and quantum capabilities at scale, necessitating sophisticated management of software pipelines to ensure that performance tracking can handle the data throughput requirements of production environments. This shift is driving a sectoral demand for researchers capable of establishing cross-platform verification methods to assess the true utility of disparate quantum systems.
Workforce scarcity is particularly acute at the intersection of domain-specific industrial variables and quantum information science. As organizations transition through varying TRLs, the ecosystem requires specialized architects who can navigate the fragmentation of the software stack and implement rigorous benchmarking frameworks. Industry dynamics, influenced by national technology strategies and private capital allocation, place a premium on roles that can drive the convergence of academic research pathways with the practical constraints of cloud-based delivery. This structural layer of expertise is the primary mechanism for maintaining technical momentum and ensuring that emerging algorithms are architecturally compatible with existing infrastructure.
Integration with scientific HPC environments remains a high-risk dependency for the sector. The evolution of the value chain depends on the ability to translate complex optimization and machine learning problems into quantum-native formulations without disrupting established enterprise workflows. Consequently, the availability of senior researchers who can orchestrate these cross-functional dependencies is a primary determinant of whether a commercial organization can successfully transition from exploratory research to scalable deployment. These efforts are essential for establishing the credibility needed to secure long-term participation in the emerging quantum-as-a-service (QaaS) market.
The capability architecture for this role type centers on the synchronization of advanced quantum algorithmic development with the protocols of software systems engineering. Mastery of the hardware-agnostic software layer is essential for ensuring that hybrid algorithms are optimized for specific hardware constraints, such as coherence times and gate fidelities. This requires a deep understanding of the interface points between high-level application programming interfaces (APIs) and the underlying quantum compilers that manage the execution of complex circuits. Expertise in statistical physics and Monte Carlo sampling methods provides the theoretical leverage needed to analyze performance data and identify promising areas for operational improvement.
These capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of research initiatives alongside the development of scalable software pipelines. By establishing rigorous verification and validation frameworks, this function provides the necessary data to assess the commercial readiness of quantum processors before full-scale capital allocation. Furthermore, the ability to communicate technical findings across diverse stakeholder landscapes ensures that scientific outputs are reconciled with practical business constraints. Such expertise reduces iteration friction between fundamental research and product delivery, which is critical for long-term interoperability within the rapidly evolving quantum ecosystem. - Accelerates the deterministic transition from theoretical research to industrial-grade quantum performance metrics
- Mitigates systemic execution risks by synchronizing algorithmic benchmarks with long-term technology roadmaps
- Facilitates the integration of quantum computational kernels into standardized high-performance computing infrastructures
- Strengthens the reliability of technology strategies through the implementation of rigorous characterization protocols
- Reduces iteration friction between fundamental physics breakthroughs and the deployment of scalable hybrid workflows
- Optimizes the allocation of technical talent across research, development, and benchmarking portfolios
- Enhances the stability of the quantum software value chain by providing predictable performance frameworks
- Supports the scaling of computational capabilities by managing the complex dependencies of quantum-classical integration
- Improves the transparency of technology readiness level progression for institutional investors and policy makers
- Enables the structural reproducibility of quantum experiments through the standardization of benchmarking protocols
- Protects high-capital research and development investments by ensuring alignment between discovery and scalability
- Orchestrates the convergence of academic research with the practical demands of global enterprise-ready servicesIndustry Tags: Quantum Benchmarking, Hybrid Algorithms, Performance Characterization, Algorithm Development, TRL Progression, Software Pipelines, Statistical Physics, Quantum-Classical Integration, Deep Tech Analysis
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