Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

QuEra Computing Inc.; Boston (MA); On-site; USD 90,000 - 130,000; Full time

Job summary

QuEra Computing Inc. in Boston, MA seeks a Postdoctoral Fellow in Applied Machine Learning in Quantum Systems to advance control for neutral-atom LQPUs, interfacing quantum hardware and software engineering.

The role emphasizes measurement-informed feedback, ML-based inference, and collaboration across physics, hardware, and control teams to enable reliable quantum operations.

Qualifications

• PhD or equivalent in Physics, CS, EE or related field; strong background in quantum computing/quantum physics.
• Experience with quantum computing platforms (neutral atoms, trapped ions, superconducting qubits, etc.).
• Experience applying ML for inference and hardware-in-the-loop scenarios.
• Proficient in Python, C++, or similar languages.
• Strong analytical/problem-solving and technical ownership.
• Effective communication and collaboration across physics, engineering and software teams.
• Proficient with Git and modern collaborative workflows.
• Publications or significant technical contributions in relevant areas.

Responsibilities

• Develop and deploy ML models for high-fidelity quantum operation inference and control prediction.
• Design in-the-loop control mechanisms that adapt sequences based on measurements and state.
• Collaborate with physics, QA, hardware, and control teams to validate components using experimental data.

Skills

ML inference

Hardware-in-the-loop

Analytical thinking

Communication

Cross-functional collaboration

Education

PhD or equivalent in Physics / CS / EE

Tools

Python

C++

Git

Job description

Postdoctoral Fellow - Applied Machine Learning in Quantum Systems at QuEra Computing, Boston, MA, USA. A executive-level full time position in the Quantum Systems team, listed under software engineering.

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 workflows.
• Track record of publications or significant technical contributions in relevant areas.

Preferred Qualifications

• Experience with quantum control stacks or