Sr. Engineer / Lead Sr. Engineer, Data Analytics and ML Model

Sr. Engineer / Lead Sr. Engineer, Data Analytics and ML Model Development

General Summary:

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Hardware Engineer, you will plan, design, optimize, verify, and test electronic systems, bring-up yield, circuits, mechanical systems, Digital/Analog/RF/optical systems, equipment and packaging, test systems, FPGA, and/or DSP systems that launch cutting-edge, world class products. Qualcomm Hardware Engineers collaborate with cross-functional teams to develop solutions and meet performance requirements.

Minimum Qualifications:

Bachelor's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 3+ years of Hardware Engineering or related work experience. ORMaster's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 2+ years of Hardware Engineering or related work experience. ORPhD in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 1+ year of Hardware Engineering or related work experience.

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:

• Masters degree in Computer Science, Data Science, or a related field.
• 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).
• Experience with deep neural network architectures including RNNs, and Transformers.
• Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• GenAI, LLMs, RAG Optimization. LLM Finetuning, Distillation Experience.
• Proficiency in data analysis using Pandas, NumPy, and Matplotlib.
• Statistical Analysis & Visualization
• Feature Engineering & Model Evaluation
• Understanding of GPU, CUDA & High performance Computing Frameworks & Methods.

Preferred Qualifications:

• PHD in Computer Science, Data Science, or a related field.
• Experience in large Model Development & Training from the Scratch.
• Familiarity with time series analysis methods including ANOVA, ARIMA, and seasonal decomposition.
• Familiarity with SQL databases, ETL frameworks, and cloud platforms (e.g., AWS).
• Knowledge of LLM integration frameworks (e.g., LangChain).
• Hands-on experience with distributed computing and cloud-scale data processing.
• Software Design & Architecture