Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

$110K-$120K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

3% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Postdoctoral Fellow - Applied Machine Learning in Quantum Systems Boston, MA USA Summary This role focuses on developing computational methods for in-the-loop stabilization of neutral atom Logical Quantum Processing Units (LQPUs). The position sits at the interface of quantum hardware and control systems, supporting both near-term experimental performance and the long-term development of control architectures for stable, fault-tolerant computing. The successful candidate will design and prototype state-of-the-art methods to enable reliable quantum operations and translate device measurements into actionable control feedback. Responsibilities include advancing capabilities such as measurement-informed feedback control and machine learning-driven inference. Key Responsibilities - Develop and deploy machine learning models for high-fidelity quantum operation inference and control prediction. - Design and prototype in-the-loop control mechanisms that adapt sequences based on measurement outcomes and system state. - Collaborate with physics, quantum error-correction, hardware, and control teams to validate new stack components using experimental data and system-level performance Required Qualifications - Ph.D. or equivalent experience in Physics, Computer Science, Electrical Engineering, or a related field, with a strong background in quantum computing or quantum physics. - Experience working with quantum computing platforms (neutral atoms, trapped ions, superconducting qubits, or similar). - Demonstrated experience working with Machine Learning for inference and hardware in loop. - Strong programming skills in Python, C++, or similar languages. - Strong analytical and problem-solving skills, with the ability to take technical ownership. - Effective communication skills and the ability to collaborate across physics, engineering, and software teams. - Proficiency with Git and modern collaborative development

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