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AI/ML Engineer (Industrial / Manufacturing) - Semiconductor
Software & AI Engineering
Singapore
Job Description
We are seeking an AI/ML Engineer (Industrial / Manufacturing) to serve as a key architect for our AI Intelligence Layer. In this role, you will design and deploy the core machine learning models that give our semiconductor equipment autonomous execution capabilities—enabling real-time parameter self-adjustment, dynamic error compensation, and root-cause yield correlation across complex manufacturing lines.
Key Responsibilities
• Autonomous Process AI: Design and implement real-time, closed-loop machine learning algorithms ("self-driving" recipes) that dynamically adjust process parameters in real time without manual operator intervention.
• Dynamic Error Compensation: Develop data-driven predictive models (e.g., Gaussian processes, neural networks, or reinforcement learning) to compensate for thermal drift, stage micro-vibrations, and parasitic motion errors on the fly.
• Yield & Defect Data Correlation: Build end-to-end data analytics and ML pipelines that correlate granular defect data across AOI, overlay metrology, and inline processing equipment sensor feeds to automatically identify root causes and yield limiters.
• Model Deployment & Inference: Deploy light-weight, low-latency ML models into edge compute nodes and high-performance communication architectures for real-time sub-millisecond execution.
• Cross-Functional Integration: Collaborate closely with Control Systems Engineers, Vision/Metrology Specialists, and Software Architects to translate physical equipment dynamics into scalable AI models.
Key Responsibilities
• Autonomous Process AI: Design and implement real-time, closed-loop machine learning algorithms ("self-driving" recipes) that dynamically adjust process parameters in real time without manual operator intervention.
• Dynamic Error Compensation: Develop data-driven predictive models (e.g., Gaussian processes, neural networks, or reinforcement learning) to compensate for thermal drift, stage micro-vibrations, and parasitic motion errors on the fly.
• Yield & Defect Data Correlation: Build end-to-end data analytics and ML pipelines that correlate granular defect data across AOI, overlay metrology, and inline processing equipment sensor feeds to automatically identify root causes and yield limiters.
• Model Deployment & Inference: Deploy light-weight, low-latency ML models into edge compute nodes and high-performance communication architectures for real-time sub-millisecond execution.
• Cross-Functional Integration: Collaborate closely with Control Systems Engineers, Vision/Metrology Specialists, and Software Architects to translate physical equipment dynamics into scalable AI models.
Job Requirements
• Education: Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Electrical Engineering, Mechatronics, or a related quantitative field.
• Experience: 3+ years of experience applying machine learning or statistical modeling to physical, industrial, or hardware-in-the-loop systems (e.g., semiconductor tools, robotics, industrial IoT, or precision equipment).
• Core ML & Data Science: Deep proficiency in supervised/unsupervised learning, time-series analysis, reinforcement learning, anomaly detection, and predictive modeling.
• Software Stack: Expert-level programming in Python (PyTorch, TensorFlow, Scikit-learn, Pandas) and experience/familiarity with C++ or Rust for high-performance deployment environments.
• System Integration: Experience working with high-frequency sensor streams, industrial protocols, and distributed low-latency communication frameworks (e.g., gRPC, MQTT, ZeroMQ).
Preferred Qualities
• Prior exposure to semiconductor yield enhancement, overlay metrology, Automated Optical Inspection (AOI), or other semiconductor process control.
• Adaptable team player who thrives in high-ownership, fast-paced startup environments and excels at turning abstract equipment data into actionable intelligence.
• Experience: 3+ years of experience applying machine learning or statistical modeling to physical, industrial, or hardware-in-the-loop systems (e.g., semiconductor tools, robotics, industrial IoT, or precision equipment).
• Core ML & Data Science: Deep proficiency in supervised/unsupervised learning, time-series analysis, reinforcement learning, anomaly detection, and predictive modeling.
• Software Stack: Expert-level programming in Python (PyTorch, TensorFlow, Scikit-learn, Pandas) and experience/familiarity with C++ or Rust for high-performance deployment environments.
• System Integration: Experience working with high-frequency sensor streams, industrial protocols, and distributed low-latency communication frameworks (e.g., gRPC, MQTT, ZeroMQ).
Preferred Qualities
• Prior exposure to semiconductor yield enhancement, overlay metrology, Automated Optical Inspection (AOI), or other semiconductor process control.
• Adaptable team player who thrives in high-ownership, fast-paced startup environments and excels at turning abstract equipment data into actionable intelligence.
For interested applicants, we welcome you to submit your updated resume to [email protected] for consideration. We
appreciate your interest and look forward to hearing from you. Please note that only shortlisted candidates will be
contacted.
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