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Physics Informed Machine Learning Jobs in Downers Grove, IL

Strike Social

Senior Data Scientist

Chicago, IL · Remote

Machine Learning, Real-time Analytics, and Experimental Modeling on the Strike Marketing Cloud/Data ... Masters or PhD - Quantitative field such as Statistics, Mathematics, Physics, or Engineering

Akuna Capital

Quantitative Researcher

Chicago, IL · On-site

$145K/yr

... machine learning or related area * BS/MS/PhD degree in a technical field - Engineering, Computer Science, Math, Physics, or similar * Proven research background in academic or professional ...

Akuna Capital

Quantitative Researcher

Chicago, IL

$145K/yr

... machine learning or related area * BS/MS/PhD degree in a technical field - Engineering, Computer Science, Math, Physics, or similar * Proven research background in academic or professional ...

Pwc

AI Engineer

Chicago, IL · On-site

$55K - $187K/yr

... machine learning solutions - Conducting complex data analysis to identify patterns and trends for informed decision-making - Collaborating with internal teams to enhance data infrastructure and ...

Pwc

AI Engineer

Rosemont, IL · On-site

$55K - $187K/yr

... machine learning solutions - Conducting complex data analysis to identify patterns and trends for informed decision-making - Collaborating with internal teams to enhance data infrastructure and ...

Pwc

AI & GenAI Data Scientist-Senior Associate

Chicago, IL

$77K - $202K/yr

... informed decision-making. Responsibilities - Designing and implementing AI systems to transform raw data into actionable insights - Developing and deploying scalable AI and Machine Learning solutions ...

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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 7, 2026, the average hourly pay for physics informed machine learning in Downers Grove, IL is $20.02, according to ZipRecruiter salary data. Most workers in this role earn between $12.45 and $25.43 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?

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 Downers Grove, IL? For Physics Informed Machine Learning jobs in Downers Grove, IL, the most frequently searched job titles are:
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What cities near Downers Grove, IL are hiring for Physics Informed Machine Learning jobs? Cities near Downers Grove, IL with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you

Hardware Machine Learning PhD Research Internship

IMC

Chicago, IL

$225K/yr

Full-time, Internship

PTO

Re-posted 22 days ago

Job description

We are deploying machine learning directly onto custom hardware - and we want you to help drive it forward. This PhD internship is an opportunity to work on research that has direct impact on IMC's work tackling open problems at the frontier of low-latency ML inference and hardware acceleration.

You'll work alongside IMC engineers in one of the most demanding low-latency computing environments in the world. You'll own a focused research project from start to finish, present your findings to the team, and leave behind a prototype or benchmark that we can build on.

Your Core Responsibilities

  • Architect and develop an ML focused research project based on real-world use cases
  • Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions
  • Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems
  • Present your project to the team, deepening our collective understanding of an area of ML acceleration
  • Gain hardware design fundamentals from skilled RTL developers and learn how they apply to our industry
  • Build skills to evaluate research not only from an academic perspective, but through real-world performance constraints, engineering costs, and industry impact

Your Skills and Experience

  • Currently enrolled in a PhD program in Electrical Engineering, Computer Science, Physics, or a related field
  • Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
  • Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
  • Understanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
  • Proficiency in Python or similar languages for tooling, testing, and simulation
  • Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

You may submit one application per role each year.  We strongly encourage you to focus on applying to a single role that best matches your skills and interests.  Though you may apply to multiple roles, please note that each application will be evaluated based on the specific criteria established for that particular role. If you have already applied for this position during the current recruitment season and were not selected, you may reapply when the next recruitment season begins in 2027.

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.

Base Salary: $225,000

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