Algorithm Engineer (Semiconductor Manufacturing)

Full Time, onsite
Alpha X Technology PTE. LTD.
Kuala Lumpur Electrical/Electronic Engineering (Engineering) Full time RM 10, Malaysia

Salary undisclosed

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Alpha X is an innovative high-tech manufacturing technology company pioneering the integration of advanced automation, transportation and AI-driven solutions to revolutionize traditional manufacturing processes. We harness the power of artificial intelligence, machine learning, and robotics to optimize production efficiency, enhance product quality, and foster sustainability across diverse high-tech industries.

What you will do:

Algorithm R&D: Responsible for the design, development, validation, testing, deployment, and maintenance of algorithms in FDC (Equipment Fault Detection and Classification) /APC (Advanced Process Control) projects and products in the semiconductor manufacturing industry
Technology selection: Give the selection and R&D suggestions of FDC algorithm in the semiconductor manufacturing industry, not only considering the technical feasibility and advancement, but also connecting with different roles of product managers, pre-sales, sales and other departments, and considering how the algorithm embodies the value from the perspective of business, market and commerce
Technical competitiveness: new technology research/technical tracking of competing products to ensure that the company's FDC algorithm is in a leading position in the industry and has a competitive advantage
Solution consulting: assist pre-sales and sales to complete technical solutions; Act as a technical expert to communicate with customers according to sales/pre-sales needs to help complete orders; Work with business team to design and iterate products and gain market acceptance

What you need to have:

Professional background: Master degree or above in automation, electrical engineering, mechanical engineering, computer science, artificial intelligence or related field
Rich experience: Have at least 3 years of anomaly detection and fault classification research and development experience, systematically master the basic principles of unsupervised learning algorithms, and having experience of semiconductor manufacturing is preferred
Statistical learning skills：
- Master common statistical methods, such as principal component analysis PCA-T2/SPE, and nonlinear variants, such as KPCA, APCA, etc. Partial least squares PLS, etc
- Supervised machine learning methods, such as artificial neural network ANN, tree models, such as GBDT, Random Forest, xgboost, lightGBM, etc
- Unsupervised machine learning methods, such as self-organizing mapping neural network SOM, self-associative neural network AANN, Autoencoder, etc
Coding capability：Proficient in writing anomaly detection and classification algorithms in python, master the commonly used python machine learning, deep learning computing framework/algorithm package, such as sklearn, pyOD, keras, tensorflow, etc
APC algorithm capability：APC algorithm capability, such as R2R batch control experience is preferred, including but not limited to：
- Virtual Metrology algorithms: master machine learning regression methods (LSTM, RNN, support vector Machine SVM, artificial neural network ANN, lightGBM, etc.), and realize the prediction of quality indicators
- Operational optimization algorithms: Knowledge of common operational optimization algorithms is preferred, such as genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing algorithm (linear programming), meta heuristic algorithm, integer programming, mixed integer programming, graph theory, etc
Strong learning and communication skills
Have good coding specification and document management habits
Be able to identify common risks in project implementation and data-driven modeling, and have a sense of timely communication