Machine Learning Engineer

Machine Learning Engineer

Department:
Engineering, Research & Development

Reports to:

Metallurgical and Materials R&D Lab Manager

Location:

Grovetown, GA, USA (onsite)

Shift: First

FLSA Status:
Salary Exempt

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.

• 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

• 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
• Required skills and competencies:
  • Solid Python skills with hands‑on experience using core libraries
  • Machine learning:
    PyTorch, scikit‑learn
  • Data:
    Num Py, pandas
  • Scientific computing:
    Sci Py, Matplotlib
  • Foundational understanding of scientific computing: numerical methods, simulation concepts, or modeling of physical systems — 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 (Lang Chain, Lang Graph, 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

• 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.