Zhengyuan Quantum
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Careers

Bring your expertise
to quantum computing.

From analog ICs and quantum EDA to AI research and engineering, and from RF to quantum control and measurement, we look forward to exploring new possibilities in quantum computing with you.

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Open roles

6 open roles · Shenzhen

Analog IC Design Engineer

Full-timeShenzhen3+ yearsBachelor’s degree or above 30–50k · 13 salary payments/year

Responsibilities

  1. Design architectures and implement circuits for high-speed digital-to-analog and analog-to-digital converters (DAC/ADC) in superconducting quantum control and measurement systems, operating in GHz and higher frequency bands.
  2. Contribute to mixed-signal chain simulation, layout design and tape-out validation to ensure signal integrity and low noise.
  3. Work with the control and measurement systems team to translate analog front-end targets for bandwidth, noise, power consumption and crosstalk into actionable circuit specifications.
  4. Contribute to chip testing and debugging, completing the verification loop from design through tape-out.

Qualifications

  1. Bachelor’s degree or above in microelectronics, electronic engineering, integrated circuit design or a related field.
  2. At least 3 years of analog IC design experience, with end-to-end tape-out experience for high-speed DAC/ADC or RF front-end chips operating in GHz and higher frequency bands.
  3. Proficiency with design and simulation tools such as Cadence Virtuoso and Spectre, and familiarity with deep-submicron process nodes.
  4. Understanding of cryogenic electronics fundamentals, including noise, power consumption and thermal load, is preferred.
  5. Experience in superconducting quantum control and measurement, RF transceiver chains or high-speed SerDes is preferred.
Send your CV

EDA Engineer

Full-timeShenzhenBachelor’s degree or above 20–40k · 13 salary payments/year

Responsibilities

  1. Quantum chip EDA tool development

    Develop and iterate on our in-house superconducting quantum chip design automation tools in Python/C++, covering layout generation, parameterized device libraries, automated routing and design rule checking (DRC). Translate physical constraints in quantum chip design, such as qubit frequency spacing, coupling strengths and resonator dimensions, into executable algorithms and code.

  2. Superconducting quantum chip layout design and iteration

    Use our in-house EDA tools to design and iterate on superconducting quantum chip layouts, including the placement and routing of qubits, couplers, resonators and readout structures. Our goal is to move thousand-qubit chip layouts from manual drawing to automated generation.

  3. Design automation algorithm optimization

    Improve existing algorithms for the specific needs of quantum chip layouts, including frequency collision avoidance, routing-space constraints and crosstalk minimization, to increase routing success rates and generation speed. Existing constraint-aware frameworks in the industry can generate a 156-qubit chip layout in around 48 minutes; our goal is to go faster and support larger scales.

  4. Collaboration with chip design and control teams

    Work closely with chip design and control and measurement engineers to understand physical design requirements and turn hardware challenges into EDA tool requirements. Every line of code you write will ultimately contribute to chip yield and system performance.

Qualifications

  1. Bachelor’s degree or above in computer science, microelectronics, electronic engineering or physics.
  2. Proficiency in multiphysics simulation with COMSOL Multiphysics is preferred.
  3. Familiarity with algorithms and data structures, with the ability to turn engineering problems into programmable algorithmic solutions.
  4. Proficiency in Python and familiarity with Linux development environments and Git version control.
  5. Experience in EDA tool development, automated layout design or related fields is preferred.
  6. Strong learning ability and a willingness to develop a deep understanding of the physics of superconducting quantum chips.

Preferred experience

  1. Experience in object-oriented Python programming and independently packaging Python modules.
  2. Knowledge of the GDS layout file format and experience with layout tools such as KLayout or Cadence.
  3. Experience using or integrating electromagnetic simulation tools such as HFSS or Sonnet, with the ability to simulate electromagnetic field modules.
  4. Front-end development experience for layout GUIs, or familiarity with Flask/Django.
  5. Understanding of superconducting qubit fundamentals, including Josephson junctions, resonators and decoherence.
Send your CV

AI ResearcherFoundation Models for Mathematics and Physics & AI for Science

Full-timeShenzhenMaster’s degree or above Negotiable

Compensation: negotiable, with highly competitive stock options and compensation incentives.

Responsibilities

  1. Foundation model architectures and algorithms for mathematics and physics

    Lead or contribute to innovation in pretraining, supervised fine-tuning (SFT) and reinforcement learning (RLHF/RLAIF) algorithms for large language models (LLMs) in mathematics and physics.

  2. Symbolic reasoning and neuro-symbolic integration

    Research deep integration between foundation models and formal theorem provers such as Lean, Isabelle and Coq to improve accuracy and robustness in complex mathematical reasoning and physics derivations.

  3. Scientific multimodality and cross-modal alignment

    Explore joint multimodal modeling of formulas, code, text, charts and scientific mesh/simulation data, enabling seamless interaction between natural language and scientific computing tools such as MATLAB, Python scientific libraries and CAD/EDA tools.

  4. Frontier academic research and practical application

    Track advances in AI for Science and foundation models in China and internationally, publish at leading conferences such as ICML, NeurIPS, ICLR and KDD, and rapidly apply frontier algorithms to business use cases.

Qualifications

  1. Education

    Master’s degree or above in computer science, artificial intelligence, applied mathematics, theoretical physics, quantum computing or a related field; a PhD is preferred.

  2. Research experience

    Substantial research results or publications at top conferences in foundation model pretraining, reinforcement learning, formal reasoning or AI for Science, such as PDE solving, molecular dynamics or quantum system modeling.

  3. Engineering and algorithmic skills

    Expertise in deep learning frameworks such as PyTorch, with a strong mathematical foundation and the ability to implement algorithms.

  4. Personal qualities

    A passion for mathematical logic, exceptional independent thinking and research skills, and a collaborative approach to multidisciplinary teamwork.

What we offer

  1. A frontier field

    Work at the intersection of foundation models for mathematics and physics and deep technology, at the forefront of AI for Science.

  2. Outstanding colleagues

    Work alongside experts in physics, computing and AI from the Chinese Academy of Sciences, leading universities and major technology companies.

  3. Flat management

    An innovative team with a flat structure, substantial autonomy and room to grow.

  4. Competitive rewards

    Industry-leading compensation, stock options and comprehensive benefits.

Send your CV

AI Engineering ExpertFoundation Model Infrastructure & Training Engineering

Full-timeShenzhen3+ yearsBachelor’s degree or above Negotiable

Compensation: negotiable, with highly competitive stock options and compensation incentives.

Responsibilities

  1. Distributed training and performance optimization

    Build efficient distributed training frameworks for foundation models in mathematics and physics, optimize operators, and tune three-dimensional parallelism (data/tensor/pipeline parallelism) and mixed-precision training.

  2. Foundation model inference and deployment engineering

    Optimize foundation model inference service architectures, such as vLLM and TensorRT-LLM, and design high-concurrency, low-latency systems for mathematical and physical reasoning and intelligent agent tool use.

  3. High-quality scientific datasets

    Design and implement pipelines for cleaning, deduplication, filtering and synthetic data generation for large-scale mathematics and physics corpora, including academic papers, textbooks, mathematics problem sets, physics simulation code and open-source formalized mathematics code.

  4. Infrastructure development

    Work with the team to manage large-scale heterogeneous GPU clusters, ensuring stable and highly available model training.

Qualifications

  1. Education

    Bachelor’s degree or above in computer science, software engineering, electronic engineering or a related field.

  2. Work experience

    At least 3 years of experience in foundation model infrastructure, distributed systems or large-scale machine learning engineering; experience leading GPU cluster training and optimization is preferred.

  3. Technical expertise
    • Expertise in C++/Python and a deep understanding of low-level systems architecture.
    • Expertise in mainstream distributed training frameworks, including PyTorch, Megatron-LM, DeepSpeed and Deepspeed-Chat.
    • Familiarity with CUDA programming, foundation model quantization or inference acceleration is preferred.
  4. Engineering standards

    Exceptionally high standards for code quality, with excellent system design and troubleshooting skills.

What we offer

  1. A frontier field

    Work at the intersection of foundation models for mathematics and physics and deep technology, at the forefront of AI for Science.

  2. Outstanding colleagues

    Work alongside experts in physics, computing and AI from the Chinese Academy of Sciences, leading universities and major technology companies.

  3. Flat management

    An innovative team with a flat structure, substantial autonomy and room to grow.

  4. Competitive rewards

    Industry-leading compensation, stock options and comprehensive benefits.

Send your CV

RF Engineer

Full-timeShenzhenBachelor’s degree or above 20–40k · 13 salary payments/year

Responsibilities

  1. Design, develop and debug RF circuits.
  2. Contribute to the definition and optimization of overall RF system designs.
  3. Select RF components and carry out performance testing and validation.
  4. Work with the team on product integration and troubleshooting.

Qualifications

  1. Bachelor’s degree or above, with a technical background in RF engineering or a related field.
  2. Familiarity with common RF instruments and RF design workflows.
  3. Project experience with low-noise amplifiers, filters and similar devices.
  4. Ability to independently analyze and solve technical issues arising in projects.
  5. Good communication skills and a collaborative mindset.
Send your CV

Quantum Control and Measurement Researcher

Full-timeShenzhenPhD or above 30–50k · 14 salary payments/year

Responsibilities

  1. Design and validate quantum chip testing environments.
  2. Install and debug quantum chips.
  3. Calibrate qubit readout, single-qubit gates and two-qubit gates.
  4. Research control and measurement methods for new qubit types.
  5. Analyze decoherence and noise in quantum chips.
  6. Simulate quantum system evolution.

Requirements

  1. PhD or above in physics or a related field.
  2. Experience in quantum chip control and measurement is preferred.
  3. Experience using at least one electromagnetic simulation software package.
  4. Ability to simulate quantum systems using programming tools such as Python, MATLAB or C++.
  5. Familiarity with qubit decoherence mechanisms and noise theory is preferred.
Send your CV