Technical Lead – AI Systems & Architecture
Qualcomm
Our team is focused on applying state-of-the-art AI and ML technologies to solve complex business problems in the chip design, qualification, and debug engineering processes. We aim to enhance engineering efficiency through intelligent automation, data-driven insights, and innovative tool development.
We are looking for a passionate and technically strong engineer to join our team focused on developing advanced data analytics and machine learning models that drive innovation in chip design, qualification, and debug engineering. This role involves building scalable data pipelines, developing custom ML models, and applying statistical and deep learning techniques to extract actionable insights from complex datasets.
Key Responsibilities:
- Design and implement end-to-end data analytics workflows tailored to semiconductor engineering use cases.
- Develop, train, and optimize ML models using supervised, unsupervised, and deep learning techniques.
- Perform feature engineering, model evaluation, and hyperparameter tuning to improve model performance.
- Build scalable data ingestion and transformation pipelines using Python and cloud-native tools.
- Apply statistical analysis and visualization techniques to derive insights from large datasets.
- Conduct time series analysis using methods such as ANOVA and other statistical techniques.
- Experiment with GenAI platforms, LLMs, and RAG techniques to enhance model capabilities.
- Collaborate with cross-functional teams to integrate ML models into engineering workflows and tools.
- Document methodologies and provide technical guidance to internal teams.
Minimum Qualifications:
- Bachelor's/Master’s degree in Computer Science, Data Science, or a related field with minimum 7+ years of work experience.
- Strong programming skills in Python or C++.
- Solid understanding of data structures, algorithms, and software design principles.
- Strong analytical and problem-solving skills.
- Hands-on experience with supervised and unsupervised learning techniques (e.g., classification, clustering, dimensionality reduction).
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