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Physics Informed Machine Learning Jobs in Boston, MA

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Lead Research Scientist - Machine Learning

Woburn, MA · On-site

$174K - $220K/yr

... physics, and data science. We use our expertise and creativity to take innovative ideas from ... Experience adapting novel machine learning approaches (e.g., from academic literature) to new data ...

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How much do physics informed machine learning jobs pay per hour?

As of May 30, 2026, the average hourly pay for physics informed machine learning in Boston, MA is $21.80, according to ZipRecruiter salary data. Most workers in this role earn between $13.56 and $27.69 per hour, depending on experience, location, and employer.

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.
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Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

QuEra Computing Inc.

Boston, MA • On-site

$53.20K - $72.20K/yr

Full-time

Posted 2 days ago

Job description

Job Summary:
QuEra Computing Inc. focuses on developing computational methods for quantum systems. The role involves designing and prototyping methods for reliable quantum operations and collaborating with various teams to validate new components using experimental data.
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.
Qualifications:
Required:
• 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:
• Experience with quantum control stacks or real-time control systems.
• Familiarity with fault-tolerant protocols, logical qubit architectures, or quantum error correction frameworks.
• Experience with performance-sensitive, low-latency, or distributed systems.
• Aware of advances in fault-tolerant quantum computing, in-the-loop control, and ML-driven control strategies.
Company:
Quantum computing is moving from "one day" to "day one." QuEra Computing is leading that transition. Founded in 2018, the company is headquartered in Boston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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