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

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%
Ph.D. in Computer Science, Physics, Applied Mathematics, or a related field (preferred)
Research experience in quantum machine learning and/or quantum information science (preferred)
Experience implementing variational quantum algorithms, parameterized quantum circuits, or quantum kernel methods (preferred)
Experience developing tensor network algorithms for large-scale modeling or simulation (preferred)
Familiarity with quantum SDKs (e.g., Qiskit, Cirq, Braket, PennyLane) (preferred)
Strong publication record in machine learning, quantum computing, or computational physics (preferred)
Experience working with quantum sensor data or quantum hardware calibration workflows (preferred)

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

quantum-inspired machine learningtensor networksmachine learning modelsstructured linear algebraPythonPyTorchJAXTensorFlowHPCvariational quantum algorithms

Soft Skills

communicationcollaborationproblem-solvingmentorshipinterdisciplinary teamworkresourcefulnessadaptabilityclear communication of complex conceptseffective teamworkfast-paced work environment

Certifications

Ph.D. in Computer SciencePh.D. in PhysicsPh.D. in Applied MathematicsPh.D. in Electrical Engineering