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
You will build the software layer that helps our teams run quantum error correction experiments faster, more reliably, and with more room for exploration. You will work closely with domain experts such as quantum researchers and experimental teams, translating between what they need to test and what our systems must execute.
What you’ll actually do
- Design and implement software for quantum error correction experiments, from high level domain specific language input to lower level language output
- Build flexible and scalable data pipelines converting quantum experiment outputs into logical level observables for fault tolerance analysis and data driven decision making by experimentalists and theorists
- Develop internal modularity and configurability so researchers can adapt experiments without rewriting core components
- Support the implementation of real time decoding and feedforward for quantum error correction experiments
- Collaborate closely with domain experts through bidirectional knowledge transfer, turning research needs into clear engineering work and sharing software insights back to the team
- Participate in daily standups and weekly design and planning meetings, contributing to healthy delivery and good technical decisions
What we’re looking for
- A relevant degree in Computer Science, Engineering, Physics, or similar, or equivalent practical experience
- Strong software development experience in one or more of Python, C++, Rust, or similar languages used for high performance and scientific systems
- Experience building reliable software for complex systems, such as compiler like pipelines, experiment control, data processing, or distributed and hardware adjacent software
- Comfort working with domain experts and translating messy research questions into clear interfaces, testable code, and maintainable modules
- Good engineering habits: version control, code reviews, testing, and a focus on clarity and long term ownership
- Confidence working in ambiguity, with the ability to iterate, learn fast, and make pragmatic choices
Nice To have :
- Familiarity with quantum computing concepts, especially quantum error correction, decoding, fault tolerance, or experimental workflows
- Experience with real time systems, low latency pipelines, or control loops that require deterministic behavior
- Exposure to domain specific languages, compilers, interpreters, or intermediate representations
- Experience with experiment data modeling, telemetry, and turning raw outputs into meaningful logical level observables
- Interest in research facing engineering, where the goal is to enable discovery without slowing it down
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 | 300+ Team Members | 30+ Quantum Computers Built | 300+ Patents Filed | 10 Location Globally
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The structural maturation of the quantum computing sector has transitioned from a focus on individual qubit stability to the systemic challenge of error mitigation and fault tolerance. The Quantum Error Correction (QEC) Software Engineer represents a critical layer in the value chain, functioning as the architectural bridge between theoretical physicist-led discovery and scalable, hardware-adjacent execution. As the industry moves beyond the Noisy Intermediate-Scale Quantum era, this role type is structurally necessary to resolve the bottleneck of logical qubit overhead and real-time decoding latency. By industrializing the software protocols required for error suppression, this function directly enables the deterministic progression of Technology Readiness Levels toward utility-scale systems. Market signals from major research hubs and national initiatives underscore that the ability to automate these complex experimental cycles is now a primary determinant of a provider's path to practical quantum advantage.
The global quantum ecosystem is currently navigating a significant TRL mismatch between the availability of physical processors and the requirements for fault-tolerant applications. While hardware modalities vary from superconducting circuits to trapped ions, the universal constraint remains the high ratio of physical-to-logical qubits. To address this, the industry is witnessing a strategic shift toward integrated software-hardware co-design, where the software stack is no longer an abstraction but a fundamental component of the error-correction loop. This evolution is driven by the need for sub-microsecond decoding-feedback latencies and the standardization of benchmarking frameworks to evaluate QEC codes across diverse hardware architectures.
National quantum strategies in the US, Europe, and Asia increasingly prioritize workforce development at the intersection of high-performance computing and quantum information science. However, a systemic talent shortage persists for engineers capable of translating abstract stabilizer codes and syndrome extraction logic into reliable, production-grade software. This gap is further complicated by the fragmentation of the software ecosystem, where the lack of common intermediate representations and domain-specific languages hinders the interoperability of full-stack systems. Consequently, organizations that can build robust, modular data pipelines for fault-tolerance analysis are gaining a competitive advantage in the race to demonstrate scalable quantum-enhanced workflows.
Furthermore, the integration of quantum systems into classical high-performance computing environments necessitates a transformation of traditional software engineering practices. The lifecycle of quantum software now demands rigorous version control, automated telemetry, and real-time deterministic behavior in control loops. As the sector moves toward FASQ (Fault-Tolerant Application-Scale Quantum) computing, the focus of the global workforce is pivoting from isolated proof-of-concepts to the establishment of industry-standard architectural best practices that ensure long-term stability and reliability.
The capability architecture for this role type centers on the convergence of low-level hardware control and high-level algorithmic abstraction. At the foundational layer, mastery of scientific programming environments and systems-level languages such as Python, C++, or Rust is essential for developing the high-performance pipelines required for syndrome decoding. These capabilities matter because they determine the structural throughput of experimental cycles, allowing for the rapid iteration of error-correction protocols without manual reconfiguration of core components. This technical proficiency is coupled with an understanding of compiler-like toolchains and intermediate representations, which facilitate the translation of high-level research questions into executable hardware instructions.
Beyond code execution, the role facilitates a high-level coupling between domain experts in quantum physics and software architects. By developing modular and configurable experiment control systems, these experts ensure that research-led discoveries can be integrated into a stable, full-stack environment. This interface is critical for the implementation of real-time feedforward loops and the modeling of logical-level observables, which are the primary indicators of a system's progression toward fault tolerance. Ultimately, these capabilities enable the industrialization of quantum research, providing the stability and reproducibility required for commercial-grade adoption.
• Accelerates the deterministic progression of Technology Readiness Levels for fault-tolerant quantum systems
• Mitigates systemic risks associated with hardware noise by establishing rigorous error correction protocols
• Facilitates the transition from noisy intermediate-scale devices to standardized application-scale quantum computers
• Reduces iteration friction in experimental workflows through the deployment of modular software toolchains
• Strengthens the structural reliability of quantum-classical hybrid architectures for industrial-grade simulation
• Harmonizes theoretical quantum research with the practical requirements of scalable systems engineering
• Optimizes the throughput of syndrome extraction through the development of low-latency decoding pipelines
• Supports the scaling of logical qubit counts by identifying bottlenecks in real-time control loops
• Shortens the time-to-market for utility-scale applications by ensuring software-hardware integration at scale
• Improves the reproducibility of experimental data through the implementation of automated telemetry systems
• Protects capital-intensive investments in quantum hardware by providing expert software-led validation
• Enables the strategic orchestration of complex research initiatives across global deep-tech networks
Industry Tags: Quantum Error Correction, Fault Tolerant Computing, QEC Software Stack, Logical Qubit Scaling, Superconducting Circuits, Quantum Systems Engineering, High Performance Computing, Deep Tech R&D
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