Machine Learning Engineer

We are seeking a motivated Machine Learning Engineer to join our data science team. This role will focus on developing, implementing, and optimizing machine learning models to innovate semiconductor manufacturing processes. The ideal candidate will have strong analytical skills, knowledge of ML algorithms, and experience working with complex datasets. Key Responsibilities: Model Development & Implementation: Develop machine learning models for defect detection, predictive maintenance, and process optimization. Design deep learning models for image-based defect classification using TensorFlow, PyTorch, and Keras. Experiment with advanced algorithms like transformers and CNNs. Data Analysis & Preprocessing: Collect, clean, and preprocess structured, unstructured, time series, and image data. Perform exploratory data analysis (EDA) to optimize model inputs. Pipeline & Infrastructure Development: Build and optimize ML pipelines integrated with existing systems. Collaborate with data engineers to implement cloud-based solutions for storage, training, and deployment on AWS or Azure. Ensure scalability, reliability, and efficiency in data operations. Collaboration & Communication: Work with data scientists, engineers, and developers to identify improvements through ML. Present findings to both technical and non-technical stakeholders. Monitor and update models to maintain performance and address data changes. Research & Innovation: Stay current on advancements in machine learning, AI, and data science, applying new techniques to projects. Conduct research to explore new approaches for optimizing semiconductor manufacturing. Qualifications: Graduate degree in Computer Science, Engineering, Mathematics, or a related field. Proficiency in machine learning, deep learning, and statistical modeling. Programming in Python, C++, SQL; experience with TensorFlow, PyTorch, Keras. Experience with NumPy, Pandas, and Scikit-learn. Knowledge of cloud platforms like AWS, Azure, or Google Cloud. Experience building ML pipelines and scalable infrastructure. Familiarity with Docker and CI/CD practices. Experience with visualization tools like Power BI and Tableau. Strong problem-solving and attention to detail. Ability to communicate complex ideas effectively to diverse teams. Self-motivated with a passion for continuous learning and innovation. Preferred Experience: Prior experience in semiconductor or manufacturing data. Knowledge of MLOps and DevOps practices for model deployment. Experience with time series data and computer vision. Familiarity with Git, Jira, and MLflow for version control and model management.

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