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Physics Informed Machine Learning Jobs in Chicago, IL

Darwill, Inc.

Machine Learning Engineer

Villa Park, IL · On-site

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized ... Stay informed on emerging AI technologies and tooling (GenAI is not the primary focus of this role ...

IMC

Hardware Machine Learning Engineer

Chicago, IL

$127K - $167K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through ...

IMC Inc

Hardware Machine Learning Engineer

Chicago, IL

$200K - $225K/yr

Hardware Machine Learning Engineer Chicago, United States; New York, United States We are deploying ... Advanced degree (MS or PhD ) in EE, CS, Physics, or related field, or equivalent depth through ...

IMC

Hardware Machine Learning Engineer

Chicago, IL

$200K - $225K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through ...

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

As of Jun 13, 2026, the average hourly pay for physics informed machine learning in Chicago, IL is $20.67, according to ZipRecruiter salary data. Most workers in this role earn between $12.88 and $26.25 per hour, depending on experience, location, and employer.

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.

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 popular job titles related to Physics Informed Machine Learning jobs in Chicago, IL? For Physics Informed Machine Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Chicago, IL look for? The top searched job categories for Physics Informed Machine Learning jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Physics Informed Machine Learning jobs? Cities near Chicago, IL with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Chicago, IL as of June 2026, with employment types broken down into 82% Full Time, 14% Part Time, and 4% Contract. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $42,989 per year, or $20.7 per hour.

Quantum Software Engineer - Machine Learning

Infleqtion

Chicago, IL • On-site

$100/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 18 days ago

Job description

We are seeking a Quantum Software Engineer with expertise in quantum-inspired classical machine learning and quantum machine learning (QML). This role will focus on developing advanced ML models and algorithms that leverage tensor networks and related structured representations, supporting applications across quantum computing and quantum sensing platforms.
As a member of the Quantum Software division, you will work closely with teams spanning quantum computing and sensing hardware and algorithms to design scalable learning architectures that operate in hybrid classical-quantum workflows. The ideal candidate brings strong foundations in machine learning and mathematical physics, along with hands-on experience developing novel model architectures for structured, high-dimensional data.
This position offers the opportunity to contribute to next-generation ML techniques that bridge classical and quantum paradigms for real-world deployment.
Job Responsibilities
The duties and responsibilities outlined below include essential functions of the role. Depending on business needs, this role may perform a combination of some or all of the following duties. Duties, responsibilities, and activities may change, or new ones may be assigned:
  • 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
Required Qualifications
  • 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%

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

Benefits
  • Salary range: $135,000 to $160,000
  • 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

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