Planckianis developing the core technology to power utility-scale quantum computers.
By design, our chip architecture decouples control lines from qubit count, removing a key bottleneck in scaling. It combines the proven reliability of superconducting circuits with a breakthrough approach to qubit control, paving the way for quantum computers capable of solving the world’s most challenging problems.
We are seeking aQuantum Measurement Engineerto design, implement, and optimize high-fidelity measurement and control of superconducting qubits.
What You'll be doing
- Design, implement, and optimize qubit readout and control chains for superconducting quantum processors
- Develop measurement protocols for qubit characterization (T₁, T₂, Ramsey, echo, RB, readout fidelity, crosstalk, etc.)
- Optimize signal-to-noise ratio and measurement fidelity under realistic hardware constraints
- Develop and maintain experimental control software (Python-based stacks, FPGA or AWG integration)
- Analyze experimental data and compare results with theoretical models and simulations
- Collaborate closely with device physicists, fabrication teams, and system engineers to improve hardware performance
- Contribute to scalable measurement architectures and automation for large-qubit systems
- Document experimental procedures and results clearly for internal and external communication
Requirements
- PhD or MSc in Physics, Applied Physics, Electrical Engineering, or a related field
- At least +5y of strong hands-on experience with superconducting qubits or closely related quantum hardware.
- Practical experience with microwave engineering (GHz electronics, IQ mixers, amplifiers, VNAs)
- Solid understanding of quantum measurement theory and qubit decoherence mechanisms
- Experience operating cryogenic systems, especially dilution refrigerators
- Proficiency in Python for experiment control and data analysis
- Familiarity with lab automation and measurement frameworks
- Ability to debug complex experimental setups across hardware and software layers
Every job has its challenges, and this one is no exception. While many companies gloss over them, we believe in being upfront:
- You’ll face tough situations, especially the classic challenge: people problems. Navigating human dynamics can be tricky and requires patience and empathy.
- Some work is unglamorous, but building a great company means rolling up your sleeves. From strategic decisions to hands-on grunt work, everyone contributes to the heavy lifting.
We're not looking for perfection; we're looking for people who are ready to grow through the hard parts and help us build something that lasts.
What We Offer
- Competitive salary + benefits
- Stock options
- Flexible working hours with hybrid working
- Ego-free, merit-based environment
Planckian is an equal-opportunity employer.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The structural transition of the quantum hardware sector from laboratory-scale prototypes to utility-scale processors necessitates a specialized tier of engineering focused on the high-fidelity readout and control of qubit states. As superconducting architectures approach higher qubit counts, the ability to manage signal-to-noise ratios and decoherence within complex cryogenic environments becomes the primary determinant of system-level performance. This role type serves as a critical stabilization layer within the hardware value chain, translating fundamental device physics into reliable, automated characterization protocols. By optimizing the interface between sensitive quantum states and classical microwave electronics, these experts mitigate the architectural bottlenecks that currently limit the deterministic scaling of quantum information processors. Current market signals from the global quantum ecosystem indicate that advancements in measurement fidelity are essential for bridging the gap between noisy intermediate-scale devices and fault-tolerant computing.
The global quantum computing industry is currently navigating a pivotal maturation phase characterized by the integration of complex multi-qubit systems within high-performance computing environments. Within this ecosystem, the hardware layer remains the most capital-intensive and technically demanding segment, where the stability of superconducting qubits is a prerequisite for all subsequent software and algorithmic layers. As feature sizes approach atomic scales and control electronics face stricter requirements for accuracy, the sector is experiencing a significant demand for expertise that can harmonize microwave engineering with quantum measurement theory.
Macro-level analysis reveals that the progression of Technology Readiness Levels (TRL) for superconducting systems is frequently hindered by the lack of automated, reproducible characterization at scale. While public and private funding cycles have accelerated the diversification of hardware modalities, the integration of these systems into modular architectures requires a standardized approach to benchmarking gate fidelity and qubit decoherence. This necessity is driving a shift from isolated experimental setups toward integrated control stacks that utilize FPGA-based automation and sophisticated signal processing to handle the increasing density of control lines.
Furthermore, the industry must contend with systemic constraints in the supply chain for cryogenic and microwave components, which are essential for operating large-scale quantum processors. The ability to design measurement protocols that function reliably under realistic hardware and thermal constraints is vital for maintaining the integrity of the technology roadmap. As the ecosystem matures toward 2026, the focus is pivoting from demonstrating basic quantum operations to establishing the high-fidelity, scalable measurement architectures required for practical quantum advantage in industrial applications.
The capability architecture for this role type centers on the synchronization of cryogenic microwave engineering with advanced quantum characterization frameworks. At the foundational layer, mastery of high-frequency electronics—including IQ mixers, amplifiers, and vector network analysis—is essential for ensuring the structural integrity of readout chains within dilution refrigerators. These capabilities are critical for maximizing the signal-to-noise ratio, which directly influences the fidelity of quantum gates and the accuracy of error-characterization protocols such as Randomized Benchmarking or Ramsey sequences.
Beyond hardware interfacing, this function facilitates the development of automated software toolchains that bridge the gap between experimental data and theoretical performance models. By implementing Python-based control stacks and FPGA-integrated measurement routines, these experts enable the high-throughput characterization necessary for iterative hardware improvement. This technical coupling ensures that advancements in qubit design are rapidly validated and integrated into the broader system architecture. Such expertise is vital for maintaining interoperability across the quantum stack, allowing for the precise calibration of complex processors and the reduction of crosstalk in high-density qubit arrays.
Accelerates the deterministic progression of superconducting quantum processors toward fault-tolerant utility-scale operations
Mitigates systemic integration risks by establishing high-fidelity measurement protocols and rigorous hardware benchmarking standards
Facilitates the transition from manual laboratory characterization to automated, scalable measurement architectures for large-qubit arrays
Reduces the iteration friction between device design and experimental validation through integrated microwave engineering workflows
Strengthens the reliability of quantum-classical hardware interfaces by optimizing signal-to-noise ratios under cryogenic constraints
Harmonizes fundamental quantum measurement theory with the practical requirements of industrial-grade experimental control software
Optimizes the throughput of hardware development cycles by implementing reproducible characterization and data analysis frameworks
Supports the scaling of quantum adoption by ensuring the stability and fidelity of underlying superconducting qubit architectures
Shortens the development timeline for high-density processors by addressing the control-line bottleneck through decoupled architectural strategies
Improves the precision of qubit calibration, directly enhancing the accuracy of algorithmic execution on noisy hardware
Protects strategic investments in quantum hardware by providing expert technical validation of emerging chip architectures
Enables the orchestration of complex cross-functional efforts between device physicists, fabrication teams, and system architects
Industry Tags: Superconducting Qubits, Quantum Measurement Theory, Microwave Engineering, Cryogenic Systems, Qubit Characterization, Hardware Scalability, Signal Processing, Lab Automation, Planckian
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