The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver ... Preferred : • Master's Degree or PhD in relevant field. • Familiarity with physics-informed ML ...
The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver ... Preferred : • Master's Degree or PhD in relevant field. • Familiarity with physics-informed ML ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Familiarity with physics-informed ML approaches, embedding physical constraints in model ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible ...
... Physics, or a related field and 4 years in any job title involving algorithm, machine learning framework, and platform development experience in data science. Alternatively, the employer will accept ...
... Physics, or a related field and 4 years in any job title involving algorithm, machine learning framework, and platform development experience in data science. Alternatively, the employer will accept ...
AI Engineer
Milwaukee, WI · On-site
$50K - $112K/yr
... informed decision-making and driving business growth. Within our Internal Firm Services practice ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...
AI Engineer
Milwaukee, WI · On-site
$50K - $112K/yr
... informed decision-making and driving business growth. Within our Internal Firm Services practice ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...
Data Scientist Analyst
Waukesha, WI · On-site +1
Requires a Bachelor's degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 1 or more years of experience ...
Data Scientist Analyst
Waukesha, WI · On-site +1
Requires a Bachelor's degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 1 or more years of experience ...
Data Scientist Analyst
Waukesha, WI · On-site +1
Requires a Bachelor's degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 1 or more years of experience ...
Data Scientist Analyst
Waukesha, WI · On-site +1
Requires a Bachelor's degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 1 or more years of experience ...
CTIO - AI Engineer- Senior Manager
$91K - $321K/yr
... informed decision-making and driving business growth. Within our Internal Firm Services practice ... the implementation of machine learning algorithms and predictive modeling techniques ...
New
CTIO - AI Engineer- Senior Manager
$91K - $321K/yr
... informed decision-making and driving business growth. Within our Internal Firm Services practice ... the implementation of machine learning algorithms and predictive modeling techniques ...
New
CTIO AI Engineering Manager
$73K - $244K/yr
They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...
CTIO AI Engineering Manager
$73K - $244K/yr
They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...
Data Automation Manager
Milwaukee, WI · On-site
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Data Automation Manager
Milwaukee, WI · On-site
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Data Automation Manager
Milwaukee, WI · On-site
$120 - $160/hr
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Data Automation Manager
Milwaukee, WI · On-site
$120 - $160/hr
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Data Automation Manager
Milwaukee, WI · On-site
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Data Automation Manager
Milwaukee, WI · On-site
You'll design and implement AI-powered automation solutions using machine learning, intelligent ... Balances thorough analysis with wisdom, experience, and judgment for informed decision-making.
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Senior Engine Code Engineer
Waukesha, WI · On-site
$104K - $143K/yr
Apply data analysis techniques, including Matlab/Simulink modeling and Python-based scripting and machine learning, to support code performance forecasting. * Stay informed on relevant technologies ...
Physics Informed Machine Learning information
See Milwaukee, WI salary details
$5.21 - $7.02
0% of jobs
$7.02 - $8.83
0% of jobs
$8.83 - $10.64
0% of jobs
$10.64 - $12.45
24% of jobs
$12.53 is the 25th percentile. Wages below this are outliers.
$12.45 - $14.25
16% of jobs
$14.25 - $16.06
0% of jobs
$16.06 - $17.87
0% of jobs
$17.87 - $19.68
0% of jobs
$19.68 - $21.49
0% of jobs
The median wage is $21.92 / hr.
$21.49 - $23.30
40% of jobs
$23.30 - $25.11
19% of jobs
$5
$19
$25
How much do physics informed machine learning jobs pay per hour?
As of Aug 20, 2026, the average hourly pay for physics informed machine learning in Milwaukee, WI is $19.77, according to ZipRecruiter salary data. Most workers in this role earn between $12.31 and $25.10 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 are popular job titles related to Physics Informed Machine Learning jobs in Milwaukee, WI?
For Physics Informed Machine Learning jobs in Milwaukee, WI, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Milwaukee, WI look for?
The top searched job categories for Physics Informed Machine Learning jobs in Milwaukee, WI are:
What cities near Milwaukee, WI are hiring for Physics Informed Machine Learning jobs?
Cities near Milwaukee, WI with the most Physics Informed Machine Learning job openings:
Applied Machine Learning Engineer II - Advanced Engineering & Technology
Brookfield, WI • On-site
Full-time
Re-posted 13 days ago
Job description
Job Summary:
Milwaukee Tool is a company that values innovation and culture, seeking to create disruptive technologies and solutions. The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver innovative technologies and accelerate product development in the Power Tool Accessories business unit.
Responsibilities:
• Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
• Frame engineering problems as ML problems by assessing ML value versus physics‑based or analytical approaches and defining practical success criteria.
• Design, train, evaluate, and deploy ML models to solve applied science and engineering problems that expand product development capabilities.
• Build end‑to‑end ML workflows spanning data acquisition, feature engineering, model development, validation, and deployment (PyTorch, TensorFlow, CUDA, Azure ML).
• Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
• Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
• Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs.
• Identify and assess emerging technologies via literature, universities, conferences, and vendor engagement.
Qualifications:
Required:
• BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
• +3 or more years of experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
• Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems.
• Strong working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit‑learn), with working knowledge of SQL.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow) and familiarity with cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
• Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
• Demonstrated ability to formulate ambiguous engineering or scientific problems into well-defined ML problems with clear objectives and evaluation criteria.
• Curiosity‑driven approach to learning new technologies and methods, with emphasis on applying machine learning to real‑world scientific and engineering challenges.
• Ability to work across a diverse range of data types.
• Hands-on approach to collaboration and evaluation of technologies.
• Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
• Ability to travel 10% of the time (domestic and international).
Preferred:
• Master’s Degree or PhD in relevant field.
• Familiarity with physics-informed ML approaches, embedding physical constraints in model architecture, or surrogate modeling for simulation acceleration.
• Experience with computer vision for engineering applications.
• Exposure to edge deployment: model optimization containerized deployment to industrial hardware.
• Experience with design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
• Familiarity with version control, experiment tracking, and reproducible research practices.
Company:
Milwaukee Tool manufactures electric power tools and accessories. It is a sub-organization of Techtronic Industries. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .
Milwaukee Tool is a company that values innovation and culture, seeking to create disruptive technologies and solutions. The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver innovative technologies and accelerate product development in the Power Tool Accessories business unit.
Responsibilities:
• Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
• Frame engineering problems as ML problems by assessing ML value versus physics‑based or analytical approaches and defining practical success criteria.
• Design, train, evaluate, and deploy ML models to solve applied science and engineering problems that expand product development capabilities.
• Build end‑to‑end ML workflows spanning data acquisition, feature engineering, model development, validation, and deployment (PyTorch, TensorFlow, CUDA, Azure ML).
• Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
• Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
• Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs.
• Identify and assess emerging technologies via literature, universities, conferences, and vendor engagement.
Qualifications:
Required:
• BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
• +3 or more years of experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
• Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems.
• Strong working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit‑learn), with working knowledge of SQL.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow) and familiarity with cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
• Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
• Demonstrated ability to formulate ambiguous engineering or scientific problems into well-defined ML problems with clear objectives and evaluation criteria.
• Curiosity‑driven approach to learning new technologies and methods, with emphasis on applying machine learning to real‑world scientific and engineering challenges.
• Ability to work across a diverse range of data types.
• Hands-on approach to collaboration and evaluation of technologies.
• Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
• Ability to travel 10% of the time (domestic and international).
Preferred:
• Master’s Degree or PhD in relevant field.
• Familiarity with physics-informed ML approaches, embedding physical constraints in model architecture, or surrogate modeling for simulation acceleration.
• Experience with computer vision for engineering applications.
• Exposure to edge deployment: model optimization containerized deployment to industrial hardware.
• Experience with design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
• Familiarity with version control, experiment tracking, and reproducible research practices.
Company:
Milwaukee Tool manufactures electric power tools and accessories. It is a sub-organization of Techtronic Industries. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .