Quantum Software Engineer - Machine Learning at Infleqtion

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

27 May, 26

Salary

160000.0

Posted On

26 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Quantum Software Engineering, Machine Learning, Quantum Machine Learning, Tensor Networks, Hybrid Workflows, Quantum Computing, Quantum Sensing, Model Architectures, Mathematical Physics, Python, PyTorch, JAX, TensorFlow, Scientific Computing, HPC, Linear Algebra

Industry

Computers and Electronics Manufacturing

Description

We are seeking a Quantum Software Engineer with expertise in quantum-inspired classical machine learning and quantum machine learning (QML). This role will focus on developing advanced ML models and algorithms that leverage tensor networks and related structured representations, supporting applications across quantum computing and quantum sensing platforms. As a member of the Quantum Software division, you will work closely with teams spanning quantum computing and sensing hardware and algorithms to design scalable learning architectures that operate in hybrid classical–quantum workflows. The ideal candidate brings strong foundations in machine learning and mathematical physics, along with hands-on experience developing novel model architectures for structured, high-dimensional data. This position offers the opportunity to contribute to next-generation ML techniques that bridge classical and quantum paradigms for real-world deployment. Job Responsibilities The duties and responsibilities outlined below include essential functions of the role. Depending on business needs, this role may perform a combination of some or all of the following duties. Duties, responsibilities, and activities may change, or new ones may be assigned: Develop and implement quantum-inspired machine learning models, including tensor network–based architectures (e.g., MPS/TTN/PEPS-inspired models) for structured data analysis Design and evaluate quantum machine learning algorithms suitable for near-term and fault-tolerant quantum computers Build hybrid classical–quantum workflows integrating classical ML pipelines with quantum processors and/or quantum sensors Develop ML models for signal processing, state estimation, calibration, and noise mitigation in quantum sensing systems Collaborate with hardware and experimental teams to translate physical system characteristics into learning-based models Optimize models for performance, scalability, and deployment in HPC and low-SWaP environments Stay current with emerging research in tensor networks, quantum information science, and advanced ML architectures Contribute to research publications, technical reports, and conference presentations Provide technical mentorship and contribute to a collaborative, interdisciplinary research environment Required Qualifications BS or MS in Computer Science, Physics, Applied Mathematics, Electrical Engineering, or a closely related field Strong experience developing, training, and optimizing machine learning models Demonstrated experience with tensor networks, structured linear algebra, or physics-informed ML architectures Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) Experience working in scientific computing and HPC environments leveraging GPU acceleration Strong mathematical foundation in linear algebra, probability, and optimization Ability to communicate complex theoretical and experimental concepts clearly across teams and to external customers Demonstrated ability to work effectively in a collaborative, cross-functional, and fast-paced R&D environment Resourceful problem-solver with a track record of delivering research ideas into prototype or production systems Willingness to travel domestically and potentially internationally up to 10% Preferred Qualifications Ph.D. in Computer Science, Physics, Applied Mathematics, or a related field Research experience in quantum machine learning and/or quantum information science Experience implementing variational quantum algorithms, parameterized quantum circuits, or quantum kernel methods Experience developing tensor network algorithms for large-scale modeling or simulation Familiarity with quantum SDKs (e.g., Qiskit, Cirq, Braket, PennyLane) Strong publication record in machine learning, quantum computing, or computational physics Experience working with quantum sensor data or quantum hardware calibration workflows Salary range: $135,000 to $160,000 100% company-paid medical, dental, vision, short/long-term disability Employer-funded Health Savings Account Unlimited PTO 401(k) match Company-paid Life and AD&D Insurance Flexible Savings Account Paid FMLA, Maternity/Paternity Leave Employee Assistance Program Student Loan Repayment Equity Program

Responsibilities

The role involves developing and implementing quantum-inspired machine learning models, such as tensor network-based architectures, for structured data analysis and designing quantum machine learning algorithms for various quantum computing platforms. Responsibilities also include building hybrid classical-quantum workflows and optimizing models for performance and deployment in HPC environments.