Machine Learning Engineer
About The Position
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.
Requirements
- Bachelor’s degree required; master’s preferred in Computer Science, Engineering, Applied Math, Physics, or a related field
- 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
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
Benefits
- fair framework conditions for collective wages and pensions
- flexible working time models
- individual training opportunities
- best career prospects
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
