Quantum Software Engineer - Machine Learning

Infleqtion•Chicago, IL

3h•$135,000 - $160,000

About The Position

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.

Requirements

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%

Nice To Haves

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

Responsibilities

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