Machine Learning Modeling Engineer - Cell Manufacturing

Job Description

Job Responsibilities

• Design, develop, and deploy end-to-end machine learning models and pipelines for cell manufacturing process optimization 

• Build and maintain the design/process modeling frameworks, integrating data from multiple process stages into unified predictive and prescriptive models 

• Collaborate with process engineers to identify high-impact ML use cases across cell production steps 

• Develop physics-informed machine learning models that incorporate electrochemical domain knowledge 

• Work with large-scale manufacturing datasets using Python, SQL, and distributed computing frameworks 

• Build and maintain data pipelines that interface with Tesla's Manufacturing Execution System (MES) and production databases 

• Develop digital twin models to simulate process behavior and test control logic under various operating conditions 

• Apply anomaly detection, time-series forecasting, and reinforcement learning techniques to improve process stability and uptime 

• Create intuitive dashboards and visualizations to communicate model outputs to engineering and operations stakeholders 

• Establish model validation frameworks, monitor model performance in production, and drive continuous improvement 

Job Requirements

• Evidence of exceptional ability, including a strong understanding of ML fundamentals and demonstrated ability to apply them to real-world, large-scale engineering problems 

• 5+ years of hands-on experience in machine learning, data science, or a related field, preferably in a manufacturing or industrial setting 

• Ph.D. in Chemical Engineering, Mechanical Engineering, Applied Mathematics, or equivalent experience 

• Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn 

• Experience with time-series data modeling, anomaly detection, and predictive analytics 

• Solid understanding of data engineering principles, including SQL, ETL pipelines, and working with production databases 

• Familiarity with statistical modeling, experimental design, and model evaluation techniques 

• Strong written and spoken English communication skills 

• Ability to manage multiple projects simultaneously and work effectively under pressure in a fast-paced environment