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Physics Based Machine Learning Jobs in Illinois (NOW HIRING)

Harnham

Senior Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

Senior Machine Learning Engineer Remote in US $160,000 - $190,000 Base + 10% Bonus THE COMPANY ... BENEFITS The compensation package includes a competitive base salary, performance-based bonus, and ...

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Description: Description Paylocity is an award-winning provider of cloud-based HR and payroll ... Our machine learning engineering team is responsible for developing infrastructure and tooling to ...

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Description: Paylocity is an award-winning provider of cloud-based HR and payroll software ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Description: Paylocity is an award-winning provider of cloud-based HR and payroll software ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Reyes Coca-Cola Bottling

Machine Learning Engineer

Niles, IL · On-site

$53 - $72.75/hr

... machine learning models * Deploy and manage ML models in production environments using ... Company policy prohibits discrimination and harassment against any applicant or employee based on ...

Reyes Coca-Cola Bottling

Machine Learning Engineer

Niles, IL · On-site

$53 - $72.75/hr

... machine learning models * Deploy and manage ML models in production environments using ... Company policy prohibits discrimination and harassment against any applicant or employee based on ...

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Physics Based Machine Learning information

What types of projects or problems does a Physics Based Machine Learning professional typically work on?

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 is a Physics Based Machine Learning job?

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

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 Illinois? For Physics Based Machine Learning jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Physics Based Machine Learning jobs in Illinois look for? The top searched job categories for Physics Based Machine Learning jobs in Illinois are:
What cities in Illinois are hiring for Physics Based Machine Learning jobs? Cities in Illinois 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 Illinois as of June 2026, with employment types broken down into 94% Full Time, 5% Part Time, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Harnham

Chicago, IL • On-site

$107K - $147K/yr

Other

Posted 8 days ago

Job description

Senior Machine Learning Engineer

Remote in US

$160,000 – $190,000 Base + 10% Bonus


THE COMPANY

Harnham is partnering with a fintech that has built a leading fraud protection platform enabling merchants to grow confidently by eliminating fraud and delivering frictionless customer experiences. They’re a global company that processes billions of transactions annually, leveraging advanced machine learning to approve more good orders while protecting revenue.

The company combines cutting-edge technology with a deeply collaborative and mission-driven culture. Their Machine Learning team sits at the core of the product, building and maintaining the models and experimentation frameworks that power fraud detection at scale.


RESPONSIBILITIES

  • Own the end-to-end lifecycle of machine learning projects, from experimentation through deployment and production monitoring.
  • Build, maintain, and optimize production-grade machine learning models for fraud detection.
  • Design and implement scalable ML pipelines to enable rapid experimentation and model iteration.
  • Develop advanced feature engineering and statistical methodologies to improve model performance.
  • Collaborate with Product, Engineering, and Risk teams to translate business needs into ML solutions.
  • Contribute to model training, evaluation frameworks, and experimentation infrastructure.
  • Ensure robustness, scalability, and reliability of ML systems in high-volume production environments.
  • Drive best practices in testing, documentation, and model monitoring across the ML team.


SKILLS AND EXPERIENCE

  • 4–6+ years of experience in machine learning within production environments.
  • Strong foundation in machine learning theory, statistical modeling, and evaluation techniques.
  • Experience building and deploying supervised and unsupervised ML models at scale.
  • Proven track record of taking ML projects from research/prototype to production.
  • Proficiency in Python, SQL, and key machine learning libraries.
  • Experience working with distributed data processing tools such as Spark.
  • Strong communication skills, with the ability to explain technical insights to non-technical stakeholders.
  • Detail-oriented mindset with a focus on delivering measurable business impact.


PREFFERED EXPERIENCE

  • Experience in fraud detection, fintech, payments, or e-commerce domains.
  • Advanced degree (Master’s or PhD) in a quantitative field.
  • Passion for writing well-tested, production-quality code.
  • Interest in adversarial machine learning and combating fraud at scale.


BENEFITS

The compensation package includes a competitive base salary, performance-based bonus, and a comprehensive benefits package within a fast-growing, mission-driven organization.


HOW TO APPLY

Please submit your CV via the Apply link on this page to register your interest.


KEY TERMS

Machine Learning | Fraud Detection | Fintech | Payments | E-Commerce | Python | SQL | Spark | Data Science | Statistical Modeling | Feature Engineering | MLOps | Experimentation | Adversarial ML | Production ML Systems

Apply