Quantum Measurement Engineer

Planckian is 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 Engineer to 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

Quantum measurement engineering is the critical feedback loop between physical hardware and computational reliability.

As superconducting systems scale toward utility, the precision of qubit characterization and readout determines

the ultimate success of error mitigation and fault tolerance. This role translates raw physical interactions into

high-fidelity data, ensuring that architectural innovations move from prototype to production. Current market

signals emphasize the transition from individual qubit control to large-scale system automation. Measurement

engineers provide the empirical validation necessary to de-risk capital-intensive hardware roadmaps.

The superconducting quantum computing sector is currently traversing the Noisy Intermediate-Scale Quantum era, where the primary technical hurdle is no longer just qubit count, but the precision and scalability of control systems. Within the quantum value chain, the measurement layer serves as the interface between cryogenics and digital control stacks. As architectures move toward decoupling control lines from qubit density, the industry is witnessing a shift toward modularity and integrated microwave engineering. This evolution is necessary to overcome the wiring bottleneck that has historically limited the expansion of dilution refrigerator capacity and chip-level integration.

Macro-level analysis by organizations like the QED-C highlights a significant Technological Readiness Level mismatch between academic breakthroughs and industrial-grade stability. The structural bottleneck in the ecosystem often lies in the characterization phase, where manual protocols fail to meet the demands of 24/7 cloud-accessible hardware. Companies like Planckian are addressing these scalability constraints by innovating at the intersection of chip design and measurement theory. However, the scarcity of senior-level personnel with hands-on experience in high-fidelity microwave readout remains a primary risk to the timely delivery of fault-tolerant systems.

Furthermore, the trend toward hybrid classical-quantum computing requires measurement protocols that can operate with low latency and high reproducibility. This requires a transition from laboratory-scale experiments to automated characterization pipelines. Sector-wide efforts continue to address talent and integration challenges in quantum systems by standardizing measurement frameworks. The ability to achieve high-fidelity readout while maintaining thermal budgets is a key determinant of competitive advantage in the global race for utility-scale quantum advantage. Infrastructure dependencies, particularly in cryogenics and signal processing, continue to shape the developmental trajectories of European and North American quantum hubs.

The capability architecture for this role type centers on the synthesis of microwave engineering, cryogenic stability, and advanced signal processing. At the hardware-software interface, the integration of field-programmable gate arrays and arbitrary waveform generators enables the precise temporal control required for complex gate operations and characterization sequences. This technical layer is essential for maximizing the signal-to-noise ratio in superconducting environments, where minuscule thermal fluctuations can degrade coherence times. Mastery of characterization protocols—including randomized benchmarking and tomographic analysis—serves as the foundational mechanism for validating hardware performance against theoretical error models.

These capabilities facilitate a critical cross-functional coupling between fabrication engineering and algorithm development. By providing granular feedback on qubit decoherence mechanisms, measurement engineers enable the iterative refinement of materials science and chip design. Furthermore, the development of automated measurement stacks in Python-based environments ensures that experimental results are reproducible and scalable across multi-qubit arrays. This structural enablement shortens the feedback loop for hardware development, allowing for more rapid progression through successive technology readiness levels. The alignment of microwave signal synthesis with cryogenic constraints is the primary lever for achieving high-fidelity, fault-tolerant operation. - Accelerates the transition from laboratory prototypes to standardized commercial-grade quantum computing architectures.

- Mitigates systemic risks associated with qubit decoherence and readout errors in large-scale superconducting arrays.

- Strengthens the empirical foundation for error correction protocols through high-fidelity characterization of noise channels.

- Enhances the operational reliability of cloud-integrated quantum hardware by stabilizing measurement feedback loops.

- Reduces iteration friction between device physics research and system-level hardware integration.

- Facilitates the scaling of quantum processing units by optimizing control line density and thermal budgets.

- Validates architectural breakthroughs in qubit control through rigorous benchmarking against industry-standard metrics.

- Harmonizes microwave signal processing with cryogenic constraints to improve overall system throughput.

- Supports the de-risking of capital investments in quantum infrastructure via precision performance verification.

- Optimizes the lifecycle of experimental hardware by identifying and isolating sources of environmental noise.

- Catalyzes the development of automated characterization pipelines necessary for industrial-scale manufacturing.

- Ensures the continuous alignment of hardware performance with the requirements of utility-scale quantum applications.Industry Tags: Superconducting Quantum Computing, Microwave Engineering, Qubit Characterization, Cryogenic Systems, Signal Processing, Quantum Control Theory, Hardware Scaling, System Integration, Fault Tolerance, Deep Tech R&D

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