Required : • BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics ... Machine Learning. • +3 or more years of experience applying ML to physical-world engineering or ...
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$100 - $150/hr
We're seeking a Head of Machine Learning to own and scale the ML function at Prodigal. This is the ... Experience as a founding ML hire, ML lead, or applied science leader at a high‑growth AI/ML ...
Senior AI Engineer - SFL Scientific
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$107K - $139K/yr
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Qualifications PhD in machine learning, computer science, bioinformatics, or a related field. Strong machine‑learning modelling expertise with experience analysing large‑scale data sets.
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Qualifications PhD in machine learning, computer science, bioinformatics, or a related field. Strong machine‑learning modelling expertise with experience analysing large‑scale data sets.
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, and evaluate ML models to help solve well-scoped applied science and engineering ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, and evaluate ML models to help solve well-scoped applied science and engineering ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, evaluate, and deploy ML models to solve applied science and engineering problems ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, evaluate, and deploy ML models to solve applied science and engineering problems ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, and evaluate ML models to help solve well-scoped applied science and engineering ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, and evaluate ML models to help solve well-scoped applied science and engineering ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, evaluate, and deploy ML models to solve applied science and engineering problems ...
... machine learning to solve problems where no established solution exists and deliver first-of-its ... Design, train, evaluate, and deploy ML models to solve applied science and engineering problems ...
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Data Scientist
Appleton, WI · On-site
$120 - $180/hr
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$90 - $150/hr
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Requirements Education: • Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: • 3+ years of experience in AI/ML development, with a focus on Natural ...
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Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: 3+ years of experience in AI/ML development, with a focus on Natural Language Processing (NLP) and ...
Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: 3+ years of experience in AI/ML development, with a focus on Natural Language Processing (NLP) and ...
Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: 3+ years of experience in AI/ML development, with a focus on Natural Language Processing (NLP) and ...
Education: · Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: · 3+ years of experience in AI/ML development, with a focus on Natural Language ...
Quick apply
Education: · Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience: · 3+ years of experience in AI/ML development, with a focus on Natural Language ...
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Senior Machine Learning Engineer, Computer Vision
Oregon, WI · On-site
$150 - $210/hr
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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?
| Aspect | Scientific Machine Learning | Data Scientist |
|---|---|---|
| Required credentials | Advanced degrees in CS, ML, or related fields; knowledge of scientific computing | Degree in CS, statistics, or related fields; strong analytical skills |
| Work environment | Research labs, academia, industry R&D teams | Business analytics, tech companies, consulting firms |
| Industry usage | Research, scientific computing, engineering simulations | Business 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:
What job categories do people searching Scientific Machine Learning jobs in Wisconsin look for?
The top searched job categories for Scientific Machine Learning jobs in Wisconsin are:
- Remote Data Scientist Machine Learning
- Machine Learning Biomedical Engineer
- Freelance Data Scientist Machine Learning
- Freelance Google Machine Learning Engineer
- Flexible Data Science
- Ai Machine Learning Drug Discovery
- Data Scientist Machine Learning
- Volunteer Genomics Data Scientist
- Polars Data
- Remote Machine Learning
Applied Machine Learning Engineer II - Advanced Engineering & Technology
Brookfield, WI • On-site
Full-time
Re-posted 15 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 .