Machine Learning Engineer
Core
Build AI systems that learn from real industrial data to solve engineering problems at the intersection of machine learning and physical systems.
Role type
Early-career Machine Learning Engineer (Scientific Computing)
Builds
Data foundations, ML models bridging physics-based simulation with data, and applied AI tooling for R&D workflows.
Domain
Industrial engineering, scientific computing, and physical systems modeling.
Deliverable
production ML models
Required skills
Python, PyTorch, scikit-learn, NumPy, pandas, SciPy, Matplotlib, Git, Linux, numerical methods, neural networks, model training, optimization
Preferred skills
Data pipelines, metadata schemas, physics-informed ML, CFD, simulation, computational mechanics, agentic AI/LLM frameworks, Jupyter, Docker, MLflow, FastAPI, React, cloud compute (AWS/Azure), GPU training
Responsibilities
Build and maintain data foundation (ingestion, cleaning, transformation, validation), implement and train ML models, contribute to applied AI tooling, develop visualization dashboards, run experiments and report findings, bring prototype code to production quality, collaborate with engineering disciplines
Seniority
Early-career, hands-on IC