Machine Learning Engineer - Engineering Models

About The ePlane Company

Chart the Course for the Future of Flight

Roles and Responsibilities

• Conduct systematic data audits of existing simulation data including schema assessment, volume, cleanliness, and gaps; define supplementary data generation requirements
• Build and maintain data pipelines for model training, validation, and continuous retraining
• Build multi-domain model pipelines that chain individual surrogate models without manual handoff
• Develop training pipelines, architecture, and prototyping for ML algorithms
• Work on productising research prototypes
• Conduct experiments to benchmark new techniques and evaluate model behavior
• Develop systematic evaluation methodology: test sets, accuracy metrics, citation quality scoring, false positive/negative analysis
• Deploy AI tools to engineering teams with structured pilots, baseline measurement, and documented adoption outcomes

Required Qualifications

• 3+ years ML engineering with a focus on deep learning for scientific or engineering applications
• Experience training regression/emulation models on physics or simulation data (surrogate modelling or reduced order modelling)
• Strong ML stack: PyTorch or TensorFlow, Pandas, NumPy, SciPy
• Surrogate modeling via Neural Networks or Gaussian Processes for use as fast-running model proxies.
• Proven understanding of fundamental data structures and the ability to apply them to solve complex problems.
• Development experience with retrieval pipeline skills and relational databases

Preferred Qualifications

• Understanding and deployment of Reinforcement Learning based tools
• Understanding of mathematics, particularly linear algebra and probability theory
• Experience with physics-informed neural networks (PiNNs) or hybrid physics-ML models
• Experience with multi-fidelity modelling or chained model pipelines
• Modeling complex multi-physics systems of ODEs and DAEs
• Gradient-based optimization
• Automatic differentiation tools and development