Modeling & Simulation Machine Learning Engineer

Overview

Develop and apply Modeling & Simulation and Machine Learning approaches to optimize high-speed manufacturing processes and build Digital Twins for virtual qualification in the consumer goods industry.

What you'll do

• Apply modern data analysis and ML approaches in combination with engineering first principles across Mechanical, Electrical, Chemical, Metallurgical and other fields to develop and understand complex manufacturing systems.
• Develop Digital Twins for virtual qualification and real-time manufacturing and warehouse optimization.
• Develop fast surrogate models of complex engineering processes combining Finite Element Analysis, Computational Fluid Dynamics, Production System Modeling with ML and physical data.
• Work with a cross-functional team of R&D scientists and engineers with expertise in R&D, manufacturing processes and data infrastructure.
• Balance in-house development against turnkey solutions with best-in-class partners and own the build/buy recommendation and the technical due diligence behind it.
• Turn insights derived from ML and M&S into actionable recommendations that drive business value, end to end from raw material to manufacturing process to final product.

What you'll need

• Master's or PhD degree in an engineering discipline, with demonstrated practical experience and/or publications applying AI/ML in combination with engineering principles to solve complex engineering problems.
• Advanced degree in computer science, statistics, applied mathematics or a related quantitative field may be considered, provided there is demonstrated practical experience and/or publications applying AI/ML to engineering problems.
• Strong foundation in any branch of Engineering (Mechanical, Chemical, Material Science, Electrical, Metallurgical, Aerospace etc).
• Hands-on coding experience and mastery in one or more languages (Python, R, C++); knowledge of either Python or R is required.
• Experience using AI-assisted software development tools, such as GitHub Copilot, to develop, test and document engineering applications.
• Demonstrated expertise in analysis of large data sets ("big data") to deliver actionable insights.
• Demonstrated expertise in combining first principles concepts from any field of engineering with advanced ML approaches to develop surrogate models.
• Prior experience and/or strong interest in "real world" problems, including knowledge of manufacturing processes and data infrastructure.
• Strong communication skills: listen to understand the problem, build trust, explain context and potential solutions.

Nice to have

• Interest or experience with Integrated Control Systems, which run the manufacturing platforms.
• Research specialties in ML including Deep Learning; engineering background with ML specialization and/or research publications.
• Data mining (data preparation, regression, classification tools, SVM); optimization (global, local, stochastic methods); model reduction and dimensionality reduction.
• Databases and data processing.
• Unix/Linux; scientific computing in R, Python, C++, Java or Scala.
• Deep learning frameworks Keras and TensorFlow; high-performance parallel and distributed computing (CPU and GPU); data visualization; data management systems.

Details

• Location: Hyderabad office, India (APAC PSC GDOP).
• Full-time, Entry Level.