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Scientific Machine Learning Jobs in Wisconsin (NOW HIRING)

WI · On-site

$110 - $180/hr

Your role on our team As a Machine Learning Engineer, you will be a hands-on leader tasked with ... Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or ...

Machine Learning Tutor

Madison, WI · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

WI · On-site

$105.50 - $132.20/hr

Collaborate with cross‑functional teams including data scientists, software engineers, and business stakeholders to identify opportunities for leveraging machine learning techniques to drive ...

WI · On-site

$140 - $190/hr

Bachelor's degree in Computer Science, Statistics, Mathematics, or a related technical field, or equivalent practical experience * 5+ professional years of experience as a Machine Learning Scientist ...

New

$225K - $260K/yr

Work closely with ML scientists and other engineers to integrate new models, experiments, and ... Hands-on experience training machine learning models across multiple GPUs or compute nodes ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Wisconsin?

For Scientific Machine Learning jobs in Wisconsin, the most frequently searched job titles are:

Infographic showing various Scientific Machine Learning job openings in Wisconsin as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer II

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Re-posted 4 days ago


Job description

Job Summary:
Techtronic Industries - TTI is a company that values its people and culture as key to its success. The Machine Learning Engineer II will create and validate machine learning models, innovate solutions for power tools, and collaborate with cross-functional teams to enhance user experiences.
Responsibilities:
• create, develop, and validate machine learning models
• work with highly cross-functional teams
• innovate and explore new machine learning solutions
• demonstrate excellent problem-solving skills
• exhibit critical thinking
• thrive under pressure in a dynamic environment
• show strong technical communication skills
• exercise fundamental project management abilities
• take proactive ownership for projects and tasks
• understand how projects connect to broader initiatives
Qualifications:
Required:
• Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.
• Completed course work or specialization in Machine Learning and/or Data Science
• At least one year of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field
• Demonstrated experience applying fundamental machine learning algorithms and techniques in a non-coursework setting (e.g. unsupervised or supervised learning, classification/regression, dimensionality reduction, model optimization)
• Demonstrated experience with machine learning and AI methods such as CNNS, transformers, or computer vision
• Proficient developing and debugging code in Python
• Proficiency in Python, with extensive experience in common libraries (NumPy, pandas, scikit-learn, Matplotlib, etc.)
• Proficiency with at least one deep learning framework (e.g. PyTorch of Tensor Flow)
• Solid mathematical foundation in statistics, linear algebra, calculus and optimization
• Experience working with modern software development tools and version control tools
• Excellent problem-solving skills, critical thinking, and ability to work well under pressure in a dynamic environment.
• Excellent technical communication skills and fundamental project management abilities
• Demonstrated strong sense of ownership of a project or tasks and understanding of relationships to other tasks/projects
• Ability to travel up to 10% of the time (domestic and international).
Preferred:
• Master’s degree or PhD in Machine Learning or related field is preferred
• At least three years of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field (an advanced degree may count toward some experience)
• Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing
• Proven track record of developing, deploying and implementing AI or ML solutions connected to business objectives
• Proficient developing and debugging code in an embedded environment in a programming language such as C or C++
• Working knowledge of various sensor technologies (e.g. IMU, thermistors, magnetic and optical) and interfacing to microcontrollers
• Working knowledge of embedded systems architecture (HW & SW), microcontroller design and operation
• Experience with different types of data collection methods, understanding their principles and demonstrating their value in relevant environments
• Experience developing and deploying machine learning algorithms to edge environments
• Demonstrated ability to develop robust MLOps pipelines and ensure efficient deployment, monitoring and scaling of ML models
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 .