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Physics Informed Machine Learning Jobs in Cleveland, TN

IonQ

Senior Physicist, Quantum Memory R&D

Chattanooga, TN · On-site

$126K - $181K/yr

... intelligence, machine learning, cryptography, and more. IonQ is at the forefront of this ... PhD in physics, applied physics, electrical engineering, or closely related field * Work in quantum ...

EPB

Quantum Software Engineer Fellow

Chattanooga, TN

Graduate degree in Computer Science, Engineering, Mathematics, Physics, Quantum Information Science ... Machine learning * Python programming language CERTIFICATIONS & OTHER REQUIREMENTS * US citizenship ...

EPB

Quantum Software Engineer Fellow

Chattanooga, TN · On-site

Graduate degree in Computer Science, Engineering, Mathematics, Physics, Quantum Information Science ... Machine learning * Python programming language CERTIFICATIONS & OTHER REQUIREMENTS * US citizenship ...

Garden Plaza of Greenbriar Cove

Concierge

College Dale, TN · On-site

$12.75 - $16.25/hr

Must demonstrate proficiency with office machines (i.e., fax, copier/scanner) Primary Job ... informed * Oversees resident check in system (i.e., educates new residents, invites participation ...

Garden Plaza of Greenbriar Cove

Concierge

College Dale, TN

$12.75 - $16.25/hr

Must demonstrate proficiency with office machines (i.e., fax, copier/scanner) Primary Job ... informed * Oversees resident check in system (i.e., educates new residents, invites participation ...

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Physics Informed Machine Learning information

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

As of Aug 26, 2026, the average hourly pay for physics informed machine learning in Cleveland, TN is $16.79, according to ZipRecruiter salary data. Most workers in this role earn between $10.48 and $21.30 per hour, depending on experience, location, and employer.

What is a physics informed machine learning?

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 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.

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 cities near Cleveland, TN are hiring for Physics Informed Machine Learning jobs?

Cities near Cleveland, TN with the most Physics Informed Machine Learning job openings:

Physics Informed Machine Learning jobs near you

Senior Physicist, Quantum Memory R&D

IonQ

Chattanooga, TN • On-site

Full-time

Posted 11 days ago

Job description

Location: Chattanooga, TN (Onsite)
Travel: Up to 10%, domestic or international.
Job ID:  1789

The Role: 

IonQ is developing the world's most powerful full-stack quantum computer based on trapped-ion technology. We are pushing past the limits of classical physics and current supercomputing technology to unlock a new era of computing. Quantum computing has the potential to impact every area of human society for the better. IonQ's computers will soon redefine industries like medicine, materials science, finance, artificial intelligence, machine learning, cryptography, and more. IonQ is at the forefront of this technological revolution.

We are looking for a Senior Physicist for development of IonQ's silicon vacancy (SiV) quantum memory platform. As a Senior Physicist, you'll be part of a cross-functional team whose mission is to lead IonQ on its journey to build the world's best quantum technologies to solve the world's most complex problems. 

In this role, you will be an integral part of the commercialization process for IonQ's SiV quantum memories, with applications across quantum computing, networking, and sensing. You will develop and mature key electrical and optical subsystems for defect-center based quantum memory nodes. Example projects include development and integration of new hardware for integration with SiV memory nodes, and working with IonQ customers and collaborators to implement and test new applications and use-cases for quantum networks and memories.

Responsibilities:

  • Collaborate with IonQ's industry, government, and academic partners, as well as internal teams, to implement new use-cases and applications using the SiV quantum memory platform 
  • Evaluate and test new hardware (e.g. pair sources, sensors, etc.) for compatibility with SiV memory nodes, for example, to add new capabilities to the network, or to enable new distributed applications
  • Coordinate with other teams internally at IonQ and/or with external vendors to develop or improve memory system subcomponents and software as needed

Requirements:

  • PhD in physics, applied physics, electrical engineering, or closely related field
  • Work in quantum optics and quantum computing (ideally previous work with single photons and/or gate-based quantum systems)
  • Experience designing and testing complex opto-mechanical and opto-electronical setups
  • Experience in implementing applications and use-cases using quantum hardware

Preferred Qualifications:

  • Experience working with defect center and/or solid-state qubits
  • Experience in optically-mediated entanglement protocols between qubits
  • Experience in writing software for lab environments (preferred language: Python)

The total compensation package includes base, bonus, equity, and a range of benefit options found on our career site. 

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