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Physics Based Machine Learning Jobs in Chicago, IL

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through ... machine learning, and engineering shape how modern markets are traded. A stabilizing force in ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

This is a fully on-site role based in Manteno, IL focused on building innovative ML models from the ... Design and implement novel machine learning and deep learning models tailored to internal research ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through ... machine learning, and engineering shape how modern markets are traded. A stabilizing force in ...

Socket.dev

Machine Learning Engineer

Oakbrook Terrace, IL · On-site

$110 - $145/hr

Overview Darwill is a nationally recognized print and marketing communications firm based in the ... We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

Gotion Inc. is based in Silicon Valley, CA, currently building a Manufacturing facility in Manteno ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

Vizient

AVP, Machine Learning & Modeling

Chicago, IL · On-site

$156K - $290K/yr

Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ... Strong proficiency in Python, SQL, and with cloud-based AI/ML platforms (AWS SageMaker, Azure ML ...

Varsity Tutors

Machine Learning Tutor

Highland Park, IL · Remote

$18 - $40/hr

What We Look For In a Machine Learning Tutor * Advanced Subject Mastery: Deep knowledge of ... based ML through advanced deep learning and deployment. * Effective Teaching Methods: Ability to ...

Varsity Tutors

Machine Learning Tutor

Evanston, IL · Remote

$18 - $40/hr

What We Look For In a Machine Learning Tutor * Advanced Subject Mastery: Deep knowledge of ... based ML through advanced deep learning and deployment. * Effective Teaching Methods: Ability to ...

Varsity Tutors

Machine Learning Tutor

Chicago, IL · Remote

$18 - $40/hr

What We Look For In a Machine Learning Tutor * Advanced Subject Mastery: Deep knowledge of ... based ML through advanced deep learning and deployment. * Effective Teaching Methods: Ability to ...

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

As of Sep 6, 2026, the average hourly pay for physics based 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 is a physics based machine learning?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What does a physics based machine learning professional do?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What are the key skills and qualifications needed to thrive in physics based machine learning?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What are popular job titles related to Physics Based Machine Learning jobs in Chicago, IL?

For Physics Based Machine Learning jobs in Chicago, IL, the most frequently searched job titles are:

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The top searched job categories for Physics Based Machine Learning jobs in Chicago, IL are:

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Cities near Chicago, IL with the most Physics Based Machine Learning job openings:

Physics Based Machine Learning jobs near you
Infographic showing various Physics Based Machine Learning job openings in Chicago, IL as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 16% Part Time, and 5% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $42,989 per year, or $20.7 per hour.

Hardware Machine Learning Engineer

IMC

Chicago, IL • On-site

$200K - $225K/yr

Full-time

PTO

Re-posted 23 days ago

Key responsibilities

  • Architect and co-design ML models with traders, quant researchers, and software engineers, considering hardware constraints.

  • Work with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept to production.

  • Track and evaluate emerging research in neural architecture search, machine learning systems, and quantization methods to improve systems.

Job description

We are deploying machine learning directly onto custom hardware - and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.
We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.
Your Core Responsibilities
  • Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs
  • Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions
  • Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production
  • 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

Your Skills and Experience
  • 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, C++, 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

Nice to Have
  • Exposure to ML compiler infrastructure such as MLIR, TVM, XLA, or similar tools for lowering and optimizing models for hardware targets
  • Background in latency-sensitive or resource-constrained systems including high-frequency trading, particle physics data acquisition, real-time signal processing, or similar domains
  • Familiarity with functional verification methodologies (for example SystemVerilog, UVM, Cocotb)
  • Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience

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.
Salary Range
$200,000-$225,000 USD
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.

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