Machine Learning Engineer
Ksb· Mechanical Or Industrial Engineering· Jul 6, 2026 · 4mo agoPosted 113 days ago
Grovetown, GA
In office
Full-time
Mid
$125K–$170K est.
1+ years exp
Before you apply
Ghost Signal:
Open since July 6, 2026 — 113 days, and still accepting applications.
Job description
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 & Development
Reports to: Metallurgical and Materials R&D Lab Manager
Location: Grovetown, GA, USA (onsite)
Shift: First
FLSA Status: Salary Exempt
OVERVIEW :
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 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
QUALIFICATIONS:
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
SKILLS / COMPETENCIES
Required: 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
KSB 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.
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