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 a Quantum Software Engineer to join the Benchmarking and Error Reduction team. In this role, you will act as the software engineering expert within an R&D environment, leading the process of transforming experimental code into production-ready tools. You will develop accessible software libraries, such as IQM Benchmarks and our Error Reduction libraries, which measure and enhance the performance of our quantum computers. Working closely with quantum engineers, you will ensure our software solutions are scalable, reliable, and deliver tangible value to both internal stakeholders and external customers.
This role is based in Munich, Germany.
What you’ll actually do
- Bridge R&D and Production: Work with quantum engineers to transform well-tested research prototypes into robust, production-ready Python libraries.
- Library Development: Actively contribute to the development of core team libraries, focusing on areas such as quantum characterization & benchmarking (IQM Benchmarks), quantum error mitigation, and error suppression, e.g. by building user-friendly APIs for external researchers.
- Innovation & Problem Solving: Bring your ideas and experience to the team to solve challenges e.g. in benchmarking and error mitigation, advancing our progress toward quantum utility.
- Software Engineering: Take ownership of the software lifecycle, including hands-on programming, code reviews, architectural design, testing, and documentation.
- End-to-End Validation: Regularly test your developments on real quantum backends to ensure hardware compatibility. You will actively use real quantum computers to validate your solutions.
- DevOps & Quality Assurance: Ensure seamless integration into IQM’s broader software stack using CI/CD.
- Technical Coordination: Collaborate with the team to plan features and define software architectures that ensure scalability and meet product requirements.
- Cross-Functional Collaboration: Act as the interface between the R&D team and the Software Development department to align on best practices and tooling.
- Operational Excellence: Proactively identify and resolve software-related technical issues and handle maintenance tasks to ensure the stability of the team’s performance.
What we’re looking for
- Strong Python Proficiency: Demonstrated professional experience in Python programming.
- DevOps & Software Engineering: Good understanding of modern software engineering practices and commitment to quality, utilizing CI/CD pipelines, automated testing, linters, and code reviews to ensure production-grade standards.
- Quantum Software Experience: Practical experience programming with Python-based quantum SDKs (e.g., Qiskit, Cirq, PennyLane, Qrisp, or similar).
- Collaborative Mindset: A collaborative and team-first mindset is essential. You are comfortable working in a team-oriented and interdisciplinary R&D environment where physicists, engineers, and software developers work together.
- Education: MSc in Physics, Mathematics, Computer Science, or a similar field.
- Quantum Computing Know-How: A good understanding of quantum computing concepts (gates, circuits, measurements, observables).
- Familiarity with the team's technical areas of responsibility is a strong plus (quantum characterization & benchmarking, quantum error mitigation, error suppression or similar field).
- Prior experience working as a Quantum Software Engineer or in a similar role is a strong plus.
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 Software Engineers focused on benchmarking and error reduction represents a critical evolution in the deep-tech sector from laboratory experimentation to production-grade utility. As hardware modalities advance, the structural bottleneck for industrial adoption resides within the software-hardware interface, specifically in quantifying system fidelity and mitigating physical noise. This role type serves as a high-leverage translation point, converting abstract error mitigation protocols into stable, reusable software infrastructure. Market indicators from consortiums like the QED-C highlight that standardized validation and performance optimization are essential for derisking commercial deployment. By transitioning experimental physics prototypes into robust, production-ready software libraries, this function establishes the predictability required for enterprise-scale integration. Consequently, such technical expertise secures the foundation for quantum-as-a-service stability and long-term ecosystem trust.
The quantum computing landscape is undergoing a decisive shift from scientific demonstrations on a small number of noisy qubits to the integration of high-fidelity computational kernels within hybrid computing fabrics. In this context, Noisy Intermediate-Scale Quantum (NISQ) systems represent the current generation of hardware, where gate errors and environmental decoherence limit circuit depth and computational reliability. As organizations target the threshold of practical quantum advantage, the primary engineering challenge has migrated from sheer qubit accumulation to rigorous architectural benchmarking and scalable error suppression. Current industry focus lies on bridging classical and quantum capabilities at scale, necessitating a sophisticated management of the software stack to extract verifiable performance from imperfect physical backends.
Ecosystem maturity is heavily constrained by the lack of standardized benchmarking protocols and the fragmentation of early-stage software tools. The value chain demands specialized software engineers who can implement automated verification frameworks capable of tracking system degradation in real time. This operational layer is crucial because manual calibration protocols fail to scale alongside increasing processor complexity and the microsecond-scale cycle times inherent to superconducting qubits. Furthermore, national technology mandates and private investment cycles place an increasing premium on cross-platform interoperability, requiring software infrastructure that abstracts physical hardware variations into universal application programming interfaces.
Integration with existing high-performance computing environments remains a primary dependency for the broader deep-tech market. The commercial viability of early quantum processors depends on their deployment as specialized accelerators alongside classical data centers. This hybrid infrastructure requires production-grade software lifecycles characterized by continuous integration, automated hardware-in-the-loop testing, and strict quality assurance protocols. Resolving these deployment challenges represents the primary mechanism for maintaining momentum as the technology transitions through varying technology readiness levels toward fault-tolerant computing.
The capability architecture for this role type centers on the synchronization of quantum information science with enterprise-grade systems engineering. Mastery of python-based quantum software development kits is essential for interfacing with high-level compilers and managing the execution of complex algorithmic primitives. This expertise requires a deep understanding of quantum characterization protocols, including randomized benchmarking and gate fidelity estimation, which form the metrics layer for system performance. These capabilities are fundamental to the operational throughput of deep-tech organizations, as they enable the parallelization of hardware development and software optimization. By implementing robust DevOps workflows and continuous integration pipelines, this function ensures that algorithmic refinements are deterministically validated on real physical backends. This systematic approach reduces the integration friction between fundamental physics research and full-stack software deployment, which is critical for scaling cloud-accessible quantum infrastructure. - Accelerates the transition from experimental research prototypes to production-ready software libraries across the quantum value chain
- Mitigates hardware execution risks by deploying automated benchmarking frameworks on real quantum processing units
- Enhances the structural reliability of quantum computing platforms through the standardization of performance metrics
- Reduces software integration friction between physics research environments and enterprise development divisions
- Optimizes system-level computational fidelity by integrating scalable error mitigation algorithms into core runtime environments
- Facilitates the deployment of full-stack systems by bridging the gap between hardware physics and cloud infrastructures
- Minimizes validation latency through the implementation of continuous hardware-in-the-loop automated testing pipelines
- Supports the scaling of hybrid workflows by ensuring predictable software performance across distributed compute fabrics
- Strengthens cross-platform interoperability by developing unified application programming interfaces for external research communities
- Protects capital allocation in hardware development by providing precise algorithmic resource estimation data
- Drives the progression of technology readiness levels via deterministic verification and validation software protocols
- Secures long-term market trust by delivering verifiable computational results on noisy intermediate-scale hardware platformsIndustry Tags: Quantum Computing Software, Benchmarking and Characterization, Quantum Error Mitigation, Full-Stack Architecture, Python Quantum SDKs, DevOps for Deep Tech, NISQ Systems Engineering, Continuous Integration Testing
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