aimlroles

Machine Learning Engineer

KSB Open · verified Oct 22, 2026
Grovetown Mid-level Machine Learning Engineering

At a glance

Mid-level Machine Learning Engineering role at KSB. Grovetown · no salary stated · we estimate $142,875–$228,150 across the United States.

Estimated pay

KSB has not published a salary for this role. The figure below is our estimate, not theirs.

$142,875 – $228,150
What the middle half of mid-level machine learning engineering roles in the United States paid, among postings that did state a salary.
Worked out from 284 postings that stated pay, median $186,750. Last updated Oct 22, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationGrovetown
PostedJul 6, 2026 · 3mo ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build and look after datasets by bringing data in, cleaning and checking it, transforming it, and maintaining metadata standards
  • Train Python machine-learning models with PyTorch to connect physical simulations with data-driven approximations
  • Create R&D tools and dashboards that help engineering users explore model results
  • Run experiments, compare outcomes with targets, and share what you find
  • Prepare prototype code for production through tests, documentation, and version control

What you bring

  • A bachelor’s degree in computer science, engineering, applied math, physics, or a related area; a master’s is preferred
  • One to three years of professional work or substantial projects in machine learning, data engineering, or scientific computing
  • Practical Python experience with PyTorch, scikit-learn, NumPy, pandas, SciPy, and Matplotlib
  • A grounding in numerical methods, simulation, or physical-system modeling, along with neural-network training and optimization
  • Experience using Git and Linux, plus clear communication and collaborative working skills

Who this fits

This early-career role suits someone who wants to apply machine learning to engineering data and physical systems, with guidance from an experienced technical lead. It is based on-site in Grovetown, Georgia, on the first shift. The work is primarily desk-based.

From the employer

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.