At IQM, we build world-leading quantum computers for the well-being of humankind. We design systems to tackle computational challenges beyond the practical limits of classical machines. Our work sits at the edge of science and engineering. It's complex, demanding, and deeply collaborative. We turn deep research into reliable, full-stack systems that drive discoveries in fields like medicine, energy, and technology, reshaping how the world computes.
Join the team that gives quantum a heartbeat.
The work
We are looking for two Quantum Algorithms Engineers (Advanced and Senior levels) to join our Quantum Machine Learning (QML) team. These positions strengthen IQM’s leadership across the stack by advancing quantum machine learning methods and integrating them into real-world computational workflows. As part of the algorithms and applications department, the machine learning team assess, develop and improve quantum machine learning algorithms with the goal of understanding and expanding where quantum computers provide advantage; whether in terms of samples required, algorithm run time, or final predictive performance.
We design with the device in mind, incorporating circuit structure, measurement strategy, error-mitigation and correction, using in-house expertise and real hardware implementations to guide our strategy. With this, we aim to deliver meaningful quantum machine learning methods, that do not just satisfy theoretical guarantees but also can be impactful in practice.
These positions are based in Munich, Germany, although remote work may be considered. The team is distributed across Germany, France, and Finland.
What you’ll actually do
- Designing novel quantum machine learning algorithms for early fault-tolerant quantum computers.
- Assessing via detailed resource estimation algorithm efficiency, incorporating analysis from theoretical results, input from hardware and device characterisation teams, simulated and real hardware experiments.
- Development of resource reduction schemes ranging from improved transpilation techniques and exploiting hardware-specific knowledge to improved algorithm design and incorporation of classical resources.
- Strategic assessment of new and current algorithmic proposals, considering in detail required resources for implementation, valuation of application areas, and competing classical approaches.
- Incorporating the research of the quantum error correction (QEC) teams into algorithm design and shaping the requirements for the next generation of devices.
- Driving the publication of high-impact research papers, presenting at scientific conferences, and promoting results through networking and collaboration.
- Contributing to intellectual property via the filing of patents, producing technical reports, comprehensive documentation, and educational content.
- Leading collaborations with industry and academic partners, as well as internal with hardware, software, and research teams, with a focus on impact and delivering results.
- Mentoring team members through knowledge sharing, both technical and soft skills.
- Explaining and translating technical content for business partners, collaborators and clients.
What we’re looking for
- Ph.D. in theoretical physics, computer science, mathematics, or a related field, or demonstrably equivalent experience.
- At least 3 years in a postdoctoral or an equivalent industrial position, demonstrating individual research and mentorship capabilities.
- A track record of impactful publications in quantum machine learning.
- Strong background in quantum computing, quantum information, and classical computer science, including algorithm design, complexity and machine learning theory.
- Expert knowledge of quantum algorithms, including quantum signal processing, quantum phase estimation, linear combination of unitaries and amplitude amplification, as well specific quantum machine learning knowledge (e.g. Born machines, QRAM design, quantum kernels/ feature maps).
- Strong analytical, mathematical modelling, and problem-solving skills.
- Proficiency with python, quantum programming frameworks (e.g., Qiskit, Cirq, Qrisp) as well as standard software development practices (git/Github/Gitlab, unit testing, code clarity + standardisation).
- An ability to communicate complex technical concepts and results to audiences ranging from non-technical to experts in the field.
- Experience in leading collaborations, as well as providing technical contributions to cross-discipline projects.
- Knowledge of quantum error correction and superconducting architectures.
- Non-asymptotic resource estimation of either scalable or fault-tolerant algorithms / quantum gadgets.
- Demonstrable experience applying theoretical tools to practical problems, such as classical shadows, MPS/ tensor network approximations, or dynamical Lie algebras.
- Experience contributing to or producing mature software libraries, either for open-source or product purposes.
Why IQM?
- Full-stack quantum computing: From quantum hardware to software layers and beyond, we build across the full-stack.
- High-performance playground: We aim high, and we know sustainable performance only works when life outside work does too—hybrid setups, flexible hours.
- Never the smartest: Expect to learn constantly. You won't always be the smartest person in the room, and that's the point.
- Approachable leadership: Flat hierarchy, direct access. Feel free to approach any leaders. They're friendlier than they look!
- The sweet spot: Big enough to matter. Small enough to move fast. Growing between a startup and a corporation. We’re in the phase where top performers get noticed.
- Bigger than IQM: Our people build know-how for the entire quantum ecosystem. We publish papers, run hackathons, and help shape a market that's still being defined.
The future of computing won’t build itself. You might be one of the few who do.
We'll start interviews and move forward with hiring as soon as we meet strong candidates. Please submit your application soon.
600M€+ Total Funding | 400+ Team Members | 30+ Quantum Computers Built | 300+ Patents Filed | 10 Location Globally
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of specialized Quantum Machine Learning (QML) algorithmic engineering represents a critical transition in the quantum value chain from fundamental physics research to high-utility computational modeling. As the industry advances toward early fault-tolerant systems, the structural necessity for roles that bridge abstract statistical learning and hardware-constrained execution becomes paramount for achieving practical quantum advantage. This function acts as a high-leverage stabilization point within the software stack, ensuring that algorithmic proposals are reconciled with the physical limitations of current and next-generation architectures. Market signals from organizations like the Quantum Economic Development Consortium highlight that such deep-tech expertise is essential for resolving the "algorithm-to-application" gap that currently hinders widespread industrial adoption. By converting complex theoretical frameworks into resource-efficient implementations, this role secures the technical foundation for long-term enterprise readiness and competitive differentiation in the global high-performance computing landscape.
The quantum machine learning landscape is undergoing a decisive shift from laboratory-scale proof-of-concepts to the integration of robust computational kernels within industrial workflows. While hardware development continues to progress across diverse qubit modalities, the primary bottleneck for commercial utility has shifted to the algorithmic layer, specifically regarding the scalability and reproducibility of QML models. The current sector-wide focus lies on bridging classical and quantum capabilities at scale, necessitating sophisticated management of the software-hardware interface to ensure that hybrid workflows can handle the high-dimensional data requirements of production environments.
Workforce scarcity is particularly acute at the intersection of domain-specific machine learning and quantum information science. As the ecosystem moves beyond Noisy Intermediate-Scale Quantum (NISQ) era benchmarks, there is an increasing requirement for architects who can navigate the fragmentation of the quantum software stack and the lack of standardized benchmarking protocols. Current industry dynamics, influenced by strategic public-private funding and national security mandates, place a premium on roles that can drive interoperability across disparate cloud platforms. This structural layer of expertise is the primary mechanism for maintaining momentum as technology transitions through varying Technology Readiness Levels (TRLs).
Integration with existing high-performance computing (HPC) environments remains a high-risk dependency for the sector. The evolution of the value chain depends on the ability to translate complex data analysis, generative modeling, and optimization problems into quantum-native formulations without disrupting established data pipelines. Consequently, the availability of senior engineers capable of orchestrating these complex cross-functional dependencies is a primary determinant of whether an organization can successfully transition from exploratory research to the deployment of scalable, impactful solutions.
The capability architecture for this role type centers on the synchronization of advanced quantum algorithmic research with the protocols of production-grade systems engineering. Mastery of the hardware-aware software layer is essential for ensuring that machine learning models are optimized for the specific constraints of superconducting architectures, such as gate fidelities and error rates. This requires a deep understanding of the integration points between high-level programming frameworks and the underlying quantum compilers that manage hybrid classical-quantum executions.
These capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of research initiatives alongside the development of scalable cloud architectures. By establishing rigorous verification and validation frameworks, this function provides the leverage needed to assess the true business value of quantum advantage before full-scale capital allocation. Furthermore, the ability to manage complex stakeholder landscapes ensures that scientific outputs are reconciled with the practical constraints of regulatory compliance and data sovereignty. Such expertise reduces the iteration friction between abstract research and product delivery, which is critical for long-term interoperability within the emerging Quantum-as-a-Service market. - Accelerates the deterministic transition from theoretical machine learning research to industrial-grade quantum applications
- Mitigates systemic execution risks by synchronizing long-term research cycles with near-term technology roadmaps
- Facilitates the integration of quantum machine learning kernels into standardized cloud and high-performance computing infrastructures
- Strengthens the reliability of organizational technology strategies through the implementation of rigorous algorithmic benchmarking
- Reduces iteration friction between fundamental quantum breakthroughs and the deployment of scalable software architectures
- Optimizes the allocation of specialized technical talent across research, development, and strategic liaison portfolios
- Enhances the stability of the quantum software value chain by providing predictable requirement frameworks for external partners
- Supports the scaling of computational capabilities by managing the complex dependencies of hybrid quantum-classical workflows
- Improves the transparency of technology readiness level progression for stakeholders in the investment and policy sectors
- Enables the structural reproducibility of quantum experiments through the standardization of architectural implementation protocols
- Protects high-capital research and development investments by ensuring alignment between scientific discovery and commercial scalability
- Orchestrates the convergence of academic research pathways with the practical demands of global enterprise-ready servicesIndustry Tags: Quantum Machine Learning, Algorithmic Engineering, Fault-Tolerant Computing, Superconducting Architectures, Hybrid Workflows, Resource Estimation, Quantum Information Theory, Software Interoperability, Deep Tech Strategy
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