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Scientific Machine Learning Jobs in Wisconsin (NOW HIRING)

... 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 ...

Sr Machine Learning Engineer

Milwaukee, WI · On-site

$103K - $141.40K/yr

Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or ... Completed course work or specialization in Machine Learning and/or Data Science using one or more ...

$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 ...

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Scientific Machine Learning information

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 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 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:

Machine Learning Engineer I

Milwaukee Tool

Brookfield, WI

Full-time

Medical, Dental, Vision, Retirement

Posted 13 days ago


Job description

Job Description:

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time.

At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success - so we give you unlimited access to everything you need to create disruptive new technologies and solutions on our engineering teams. Our Engineering Team is responsible for giving life to the batteries, motors, and electronics that power solutions changing the lives of our users. Every developmental phase of these critical components happens in-house under the watch of this team. We continue to invest in engineering resources to design and develop leadership in electronic capabilities; something unique within the industry. And we're pushing the limits in firmware engineering, power electronics, embedded systems, machine learning, and the use of artificial intelligence.

Your role on our team

As a Machine Learning Engineer, you will be a hands-on leader tasked with deploying machine learning models in creative ways while working with highly cross-functional teams to make power tool solutions that change the lives of our users. You will act as a technical expert in the creation and execution of these concepts into products, supporting the team through implementation, validation, and transfer to production.

This role requires excellent problem-solving skills, critical thinking, and the ability to work well under pressure in a dynamic environment. You will leverage strong technical communication skills and fundamental project management abilities to ensure clarity and alignment across teams. Additionally, you will demonstrate a strong sense of ownership for projects and tasks, with a clear understanding of how they connect to broader initiatives.

What TOOLS you'll bring with you (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
  • 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)
  • Sold mathematical foundation in statistics, linear algebra, calculus and optimization
  • Ability to travel up to 10% of the time (domestic and international).

Other TOOLS you may have (Preferred):

  • Master's degree or PhD in Machine Learning or related field
  • 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 are preferred
  • Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing
  • Proficient developing and debugging code in an embedded environment in a programming language such as C or C++
  • Experience working with modern software development tools and version control tools

We provide these great perks and benefits:

  • Robust health, dental and vision insurance plans.
  • Generous 401 (K) savings plan.
  • Education assistance.
  • On-site wellness, fitness center, food, and coffee service.
  • And many more, check out our benefits site HERE.

Milwaukee Tool is an equal opportunity employer.