Senior Engineer / Lead Senior Engineer, Data Analytics & ML Model Development

Job Title

Senior Engineer / Lead Senior Engineer, Data Analytics & ML Model Development

Role Summary

Join a hardware-focused AI/ML team that applies machine learning and data analytics to chip design, qualification, and debug engineering. The role builds scalable data pipelines, develops ML models and statistical analyses, and integrates solutions into engineering workflows.

Experience Level

Senior. Experience guidance in the posting indicates typical combinations of degree and experience: Bachelor's +3+ years, Master’s +2+ years, PhD +1+ year.

Responsibilities

Work with engineering teams to design and deploy data-driven solutions for semiconductor engineering problems.

• Design and implement end-to-end data analytics workflows for 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.
• Build scalable data ingestion and transformation pipelines using Python and cloud-native tools.
• Apply statistical analysis and visualization techniques to large datasets; conduct time series analysis (e.g., ANOVA and related methods).
• Experiment with GenAI, large language models (LLMs), and retrieval-augmented generation (RAG) to enhance model capabilities.
• Collaborate with cross-functional teams to integrate ML models into engineering tools and workflows.
• Document methodologies and provide technical guidance to internal teams.

Requirements

Key technical skills and hands-on experience required for the role.

• Must-have: Strong programming skills in Python or C++.
• Solid understanding of data structures, algorithms, and software design principles.
• Hands-on experience with supervised and unsupervised learning (classification, clustering, dimensionality reduction).
• Experience with deep neural network architectures, including RNNs and Transformers, and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Experience with data analysis libraries: Pandas, NumPy, Matplotlib; feature engineering and model evaluation methods.
• Practical knowledge of GPU, CUDA, and high-performance computing frameworks.
• Experience applying statistical analysis and visualization to engineering datasets; time-series analysis experience.
• Ability to build scalable data pipelines and use cloud-native tools.
• Strong analytical and problem-solving skills.

Nice-to-have:

• Experience with GenAI/LLM fine-tuning, distillation, and RAG optimization.
• Experience with large model development and training from scratch; distributed computing and cloud-scale data processing.
• Familiarity with SQL, ETL frameworks, AWS or other cloud platforms, and LLM integration frameworks (e.g., LangChain).
• Software design and architecture experience.

Education Requirements

Posting references Bachelor’s, Master’s, and PhD degrees in Computer Science, Data Science, Electrical/Electronics Engineering, Engineering, or related fields. Experience guidance given alongside degrees: Bachelor's with ~3+ years, Master’s with ~2+ years, PhD with ~1+ year. Preferred qualifications also list a PhD in related fields.

About the Company

Company: Qualcomm

Headquarters: San Diego, California, United States

Qualcomm is a global leader in semiconductor and telecommunications equipment, specializing in mobile technologies and innovations. Known for its Adreno GPUs, the company provides solutions enabling advancements in mobile gaming, AI, VR/AR, and autonomous driving. Qualcomm's cutting-edge technology and commitment to high-performance, power-efficient designs drive the evolution of mobile graphics and connectivity worldwide.