Machine Learning Engineer

Summary

KSB is a leading supplier of pumps, valves, and related services, and they are seeking a Machine Learning Engineer to join their expanding R&D group. The role involves using machine learning to solve engineering problems by building AI systems that learn from industrial data and connect with engineering models.

Responsibilities

• Build and maintain the data foundation: ingestion, cleaning, transformation, validation, and metadata standards
• Implement and train machine learning models using Python and modern frameworks (PyTorch)
• Contribute to applied AI tooling that supports the broader R&D workflow
• Develop visualization and dashboard interfaces that present results to end users
• Run experiments, track results, and report findings against defined targets
• Help bring prototype code to production quality: testing, documentation, version control
• Collaborate with team members across engineering disciplines

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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

Benefits

• Fair framework conditions for collective wages and pensions
• Flexible working time models
• Individual training opportunities
• The best career prospects