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 ...
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 ...
Senior AI Engineer - SFL Scientific
$103K - $141K/yr
Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...
Senior AI Engineer - SFL Scientific
$103K - $141K/yr
Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...
$107K - $139K/yr
Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Bachelor's degree in Computer Science, Engineering, or equivalent experience * 7+ years of ...
$107K - $139K/yr
Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Bachelor's degree in Computer Science, Engineering, or equivalent experience * 7+ years of ...
$118K - $153K/yr
Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Bachelor's degree in Computer Science, Engineering, or equivalent experience * 7+ years of ...
$118K - $153K/yr
Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Bachelor's degree in Computer Science, Engineering, or equivalent experience * 7+ years of ...
... 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, 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, 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 ...
Principal Applied Scientist
WI · On-site +1
Algorithms, Data Analysis, Machine Learning (ML), Natural Language, Python (Programming Language), Reinforcement Learning, Researching, Scientific Writing, Statistical Models, Technical Leadership #J ...
Principal Applied Scientist
WI · On-site +1
Algorithms, Data Analysis, Machine Learning (ML), Natural Language, Python (Programming Language), Reinforcement Learning, Researching, Scientific Writing, Statistical Models, Technical Leadership #J ...
Develop and implement statistical and machine learning models * Fine-tune, optimize and ensure the ... Translate data science outputs into business outcomes and value delivered * Mentor and guide junior ...
Develop and implement statistical and machine learning models * Fine-tune, optimize and ensure the ... Translate data science outputs into business outcomes and value delivered * Mentor and guide junior ...
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 ...
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 ...
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 ...
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 ...
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 ...
Senior AI/ML Scientist
Milwaukee, WI · On-site
$143K - $229K/yr
Own delivery of large and/or complex machine learning (ML), deep learning (DL), and Generative AI ... Science, Data Visualization, Deep Learning Algorithms, Machine Learning (ML), Machine Learning ...
Senior AI/ML Scientist
Milwaukee, WI · On-site
$143K - $229K/yr
Own delivery of large and/or complex machine learning (ML), deep learning (DL), and Generative AI ... Science, Data Visualization, Deep Learning Algorithms, Machine Learning (ML), Machine Learning ...
Lead AI Platform Engineer
Madison, WI · On-site
$151K - $256K/yr
Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems. * Partner on the ...
Lead AI Platform Engineer
Madison, WI · On-site
$151K - $256K/yr
Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems. * Partner on the ...
PhD in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 5 years of applied analytical experience with demonstrated impact in ...
PhD in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 5 years of applied analytical experience with demonstrated impact in ...
MLOps Engineer II (Remote)
Menomonee Falls, WI · On-site
$97K - $134K/yr
Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation * Partner ...
MLOps Engineer II (Remote)
Menomonee Falls, WI · On-site
$97K - $134K/yr
Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation * Partner ...
Artificial Intelligence Engineer III
Madison, WI · On-site
$58 - $77.75/hr
This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...
Artificial Intelligence Engineer III
Madison, WI · On-site
$58 - $77.75/hr
This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...
Artificial Intelligence Engineer III
$58 - $77.75/hr
This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...
Artificial Intelligence Engineer III
$58 - $77.75/hr
This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...
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 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 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 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:
Applied Machine Learning Engineer II - Advanced Engineering & Technology
Brookfield, WI • On-site
Full-time
Posted 5 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. 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. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .