Technical Lead – AI Systems & Architecture

Job Title

Technical Lead – AI Systems & Architecture

Role Summary

Lead development of data analytics and machine learning solutions that improve chip design, qualification, and debug engineering workflows. The role spans model development, scalable data pipelines, and integration with engineering tools.

Work within a cross-functional engineering team to deploy ML/AI approaches (including GenAI and LLMs) that enable intelligent automation, high-performance model training, and actionable analytics for semiconductor engineering.

Experience Level

Senior — role targets experienced engineers. The posting references 7+ years for AI/ML experience; alternate hardware-specific minimums (Bachelor's+4 / Master's+3 / PhD+2 years) are also listed.

Responsibilities

Primary responsibilities focus on end-to-end ML systems, data pipelines, and integration with engineering workflows.

• 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, time series methods, and visualization to derive insights from large datasets.
• Experiment with GenAI platforms, LLMs, RAG techniques, and LLM fine-tuning/distillation.
• Collaborate with cross-functional teams to integrate models into engineering tools and workflows.
• Document methodologies and provide technical guidance to internal teams.

Requirements

Focused list of required technical skills and relevant experience. Educational degree details are summarized separately under Education Requirements.

• Must-have: Strong programming skills in Python or C++; solid understanding of data structures, algorithms, and software design principles.
• Must-have: Hands-on experience with supervised and unsupervised learning, and deep neural network architectures (including RNNs and Transformers).
• Must-have: Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch; data analysis using Pandas and NumPy; visualization with Matplotlib or equivalent.
• Must-have: Familiarity with GPU, CUDA, and high-performance computing frameworks.
• Nice-to-have: Experience with GenAI/LLMs, RAG optimization, LLM fine-tuning and distillation, LangChain or similar integration frameworks.
• Nice-to-have: Experience with SQL, ETL frameworks, cloud platforms (e.g., AWS), distributed computing, and cloud-scale data processing.
• Nice-to-have: Demonstrated software design and architecture experience and prior work on large model development and training.

Education Requirements

Degrees: Bachelor's, Master's, or PhD in Computer Science, Data Science, Electrical/Electronics Engineering, Engineering, or related technical fields are referenced. The posting cites multiple experience thresholds: a general AI/ML path referencing 7+ years of work experience; and hardware-focused minimums of Bachelor's + 4 years, Master's + 3 years, or PhD + 2 years. A PhD is listed as preferred in a separate section.

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.