Machine Learning Engineer

KSB is a leading supplier of pumps, valves and related service. Our reliable, high-efficiency products are used in applications wherever fluids need to be transported or shut off, covering everything from building services,industry and water transport to waste water treatment, power plant processes and mining. Founded in 1871 in Frankenthal, Germany, the company has a presence on all continents with its own sales and marketing organisations and manufacturing facilities. Around the globe, more than 190 service centres and around 3,500 service specialists are on hand to provide local inspection, servicing, maintenance and repair services under the KSB SupremeServ brand. Innovative technology that is the fruit of KSB’s research and development activities forms the basis for the company’s success.People. Passion. Performance. It is these three success factors that make KSB the company it is today.At KSB, we recognise that it is people who actually make the difference - the people we employ and the people we serve. This is why we are committed to equal rights and treatment worldwide and never lose sight of the aspects ecology and sustainability when manufacturing our products.Machine Learning Engineer KSB GIW, Inc.Department: Engineering, Research & DevelopmentReports to: Metallurgical and Materials R&D Lab ManagerLocation: Grovetown, GA, USA (onsite)Shift: FirstFLSA Status: Salary ExemptOVERVIEW:Our R&D group is expanding its use of machine learning to solve real engineering problems, and we’re looking for a sharp, hands-on early-career engineer to join the team.You’ll work at the intersection of machine learning and the physical world to build AI systems that learn from real industrial data and connect with the engineering models behind them. The role lives where machine learning meets scientific computing: surrogate modeling, data-driven approximations of physical systems, and ML models that respect the underlying engineering principles.You’ll build the data foundation that powers this work, implement and train models that bridge physics-based simulation with modern machine learning, and work closely with an experienced technical lead who will guide your growth across data engineering, scientific ML, and emerging AI tooling.RESPONSIBILITIES:Build and maintain the data foundation: ingestion, cleaning, transformation, validation, and metadata standardsImplement and train machine learning models using Python and modern frameworks (PyTorch)Contribute to applied AI tooling that supports the broader R&D workflowDevelop visualization and dashboard interfaces that present results to end usersRun experiments, track results, and report findings against defined targetsHelp bring prototype code to production quality: testing, documentation, version controlCollaborate with team members across engineering disciplinesQUALIFICATIONS:Education: Bachelor’s degree required; master’s preferred in Computer Science, Engineering, Applied Math, Physics, or a related fieldExperience: 1-3 years of professional or substantial project experience in machine learning, data engineering, or scientific computing SKILLS / COMPETENCIESRequired:Solid Python skills with hands-on experience using core libraries:Machine learning: PyTorch, scikit-learnData: NumPy, pandasScientific computing: SciPy, MatplotlibFoundational understanding of scientific computing: numerical methods, simulation concepts, or modeling of physical systems - this is essential to the roleFoundational understanding of neural networks, model training, and optimizationExperience with version control (Git) and working in a Linux environmentStrong written and verbal communication skillsCollaborative, coachable attitudePreferred:Experience building and maintaining data pipelines, metadata schemas, and data quality frameworksExposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models)Background in CFD, simulation, computational mechanics, or applied physicsFamiliarity with agentic AI / LLM frameworks (LangChain, LangGraph, or similar) enough to collaborate effectively, not leadExperience with Jupyter, Docker, MLflow, or FastAPIFront-end / dashboard development experience (React)Cloud compute (AWS or Azure) and GPU-based trainingCoursework or research projects in numerical methods, engineering, or applied sciencePHYSICAL REQUIREMENTS:Primarily desk-type dutyKSB Group is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. KSB makes hiring decisions based solely on qualifications, merit, and business needs at the time. We value employees who take the initiative and are committed to our company; Employees who take responsibility and for whom business success is the focus of their actions. In return, we offer fair framework conditions for collective wages and pensions, flexible working time models, individual training opportunities and the best career prospects. KSB is a leading supplier of pumps, valves and related service. Our reliable, high-efficiency products are used in applications wherever fluids need to be transported or shut off, covering everything from building services,industry and water transport to waste water treatment, power plant processes and mining. Founded in 1871 in Frankenthal, Germany, the company has a presence on all continents with its own sales and marketing organisations and manufacturing facilities. Around the globe, more than 190 service centres and around 3,500 service specialists are on hand to provide local inspection, servicing, maintenance and repair services under the KSB SupremeServ brand. Innovative technology that is the fruit of KSB’s research and development activities forms the basis for the company’s success.People. Passion. Performance. It is these three success factors that make KSB the company it is today.At KSB, we recognise that it is people who actually make the difference - the people we employ and the people we serve. This is why we are committed to equal rights and treatment worldwide and never lose sight of the aspects ecology and sustainability when manufacturing our products.Machine Learning Engineer KSB GIW, Inc.Department: Engineering, Research & DevelopmentReports to: Metallurgical and Materials R&D Lab ManagerLocation: Grovetown, GA, USA (onsite)Shift: FirstFLSA Status: Salary ExemptOVERVIEW:Our R&D group is expanding its use of machine learning to solve real engineering problems, and we’re looking for a sharp, hands-on early-career engineer to join the team.You’ll work at the intersection of machine learning and the physical world to build AI systems that learn from real industrial data and connect with the engineering models behind them. The role lives where machine learning meets scientific computing: surrogate modeling, data-driven approximations of physical systems, and ML models that respect the underlying engineering principles.You’ll build the data foundation that powers this work, implement and train models that bridge physics-based simulation with modern machine learning, and work closely with an experienced technical lead who will guide your growth across data engineering, scientific ML, and emerging AI tooling.RESPONSIBILITIES:Build and maintain the data foundation: ingestion, cleaning, transformation, validation, and metadata standardsImplement and train machine learning models using Python and modern frameworks (PyTorch)Contribute to applied AI tooling that supports the broader R&D workflowDevelop visualization and dashboard interfaces that present results to end usersRun experiments, track results, and report findings against defined targetsHelp bring prototype code to production quality: testing, documentation, version controlCollaborate with team members across engineering disciplinesQUALIFICATIONS:Education: Bachelor’s degree required; master’s preferred in Computer Science, Engineering, Applied Math, Physics, or a related fieldExperience: 1-3 years of professional or substantial project experience in machine learning, data engineering, or scientific computing SKILLS / COMPETENCIESRequired:Solid Python skills with hands-on experience using core libraries:Machine learning: PyTorch, scikit-learnData: NumPy, pandasScientific computing: SciPy, MatplotlibFoundational understanding of scientific computing: numerical methods, simulation concepts, or modeling of physical systems - this is essential to the roleFoundational understanding of neural networks, model training, and optimizationExperience with version control (Git) and working in a Linux environmentStrong written and verbal communication skillsCollaborative, coachable attitudePreferred:Experience building and maintaining data pipelines, metadata schemas, and data quality frameworksExposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models)Background in CFD, simulation, computational mechanics, or applied physicsFamiliarity with agentic AI / LLM frameworks (LangChain, LangGraph, or similar) enough to collaborate effectively, not leadExperience with Jupyter, Docker, MLflow, or FastAPIFront-end / dashboard development experience (React)Cloud compute (AWS or Azure) and GPU-based trainingCoursework or research projects in numerical methods, engineering, or applied sciencePHYSICAL REQUIREMENTS:Primarily desk-type dutyKSB Group is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. KSB makes hiring decisions based solely on qualifications, merit, and business needs at the time. We value employees who take the initiative and are committed to our company; Employees who take responsibility and for whom business success is the focus of their actions. In return, we offer fair framework conditions for collective wages and pensions, flexible working time models, individual training opportunities and the best career prospects.

• Education: Bachelor’s degree required; master’s preferred in Computer Science, Engineering, Applied Math, Physics, or a related field

• Experience: 1-3 years of professional or substantial project experience in machine learning, data engineering, or scientific computing, * Solid Python skills with hands-on experience using core libraries:
• Machine learning: PyTorch, scikit-learn
• Data: NumPy, pandas
• Scientific computing: SciPy, Matplotlib
• Foundational understanding of scientific computing: numerical methods, simulation concepts, or modeling of physical systems - this is essential to the role
• Foundational understanding of neural networks, model training, and optimization
• Experience with version control (Git) and working in a Linux environment
• Strong written and verbal communication skills
• Collaborative, coachable attitude

Preferred:

• Experience building and maintaining data pipelines, metadata schemas, and data quality frameworks
• Exposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models)
• Background in CFD, simulation, computational mechanics, or applied physics
• Familiarity with agentic AI / LLM frameworks (LangChain, LangGraph, or similar) enough to collaborate effectively, not lead
• Experience with Jupyter, Docker, MLflow, or FastAPI
• Front-end / dashboard development experience (React)
• Cloud compute (AWS or Azure) and GPU-based training
• Coursework or research projects in numerical methods, engineering, or applied science

PHYSICAL REQUIREMENTS:

• Primarily desk-type duty

We value employees who take the initiative and are committed to our company; Employees who take responsibility and for whom business success is the focus of their actions. In return, we offer fair framework conditions for collective wages and pensions, flexible working time models, individual training opportunities and the best career prospects.

KSB is a leading supplier of pumps, valves and related service. Our reliable, high-efficiency products are used in applications wherever fluids need to be transported or shut off, covering everything from building services,industry and water transport to waste water treatment, power plant processes and mining. Founded in 1871 in Frankenthal, Germany, the company has a presence on all continents with its own sales and marketing organisations and manufacturing facilities. Around the globe, more than 190 service centres and around 3,500 service specialists are on hand to provide local inspection, servicing, maintenance and repair services under the KSB SupremeServ brand. Innovative technology that is the fruit of KSB’s research and development activities forms the basis for the company’s success.

People. Passion. Performance. It is these three success factors that make KSB the company it is today.

At KSB, we recognise that it is people who actually make the difference - the people we employ and the people we serve. This is why we are committed to equal rights and treatment worldwide and never lose sight of the aspects ecology and sustainability when manufacturing our products., Our R&D group is expanding its use of machine learning to solve real engineering problems, and we’re looking for a sharp, hands-on early-career engineer to join the team.

You’ll work at the intersection of machine learning and the physical world to build AI systems that learn from real industrial data and connect with the engineering models behind them. The role lives where machine learning meets scientific computing: surrogate modeling, data-driven approximations of physical systems, and ML models that respect the underlying engineering principles.

You’ll build the data foundation that powers this work, implement and train models that bridge physics-based simulation with modern machine learning, and work closely with an experienced technical lead who will guide your growth across data engineering, scientific ML, and emerging AI tooling.