Sr. AI Engineer

Qualcomm is hiring an AI Model Training Engineer to design, train, fine-tune, and optimize machine learning models in an onsite role in Santa Clara, CA.

• Build and train machine learning and deep learning models using structured and unstructured datasets
• Fine-tune pre-trained models for downstream tasks, including object detection, classification, LLMs, and vision transformers
• Design training pipelines focused on reproducibility, efficiency, and scalability
• Run hyperparameter optimization, model evaluation, and performance tuning
• Partner with data engineering to work with high-quality, well-labeled, and balanced datasets
• Monitor training runs to identify failure modes such as overfitting, underfitting, or bias
• Stay current with latest research and incorporate state-of-the-art approaches into training workflows
• Document work including models, training strategies, and experiment results for internal knowledge sharing and compliance

Requirements

• Education and experience (choose one):
  • Bachelor’s degree in Engineering, Information Systems, Computer Science, or related field plus 2+ years of Software Engineering or related work experience
  • Master’s degree in Engineering, Information Systems, Computer Science, or related field plus 1+ year of Software Engineering or related work experience
  • PhD in Engineering, Information Systems, Computer Science, or related field
• Experience: 2+ years of academic or work experience programming with C, C++, Java, Python, or similar languages

Technologies

• C, C++, Java, Python
• PyTorch
• onnxruntime
• Hugging Face Transformers
• scikit-learn, NumPy
• GPU/TPU
• Distributed training frameworks
• LLMs, vision transformers
• Cloud-based ML platforms
• CI/CD
• Containerization
• Model versioning
• MLOps

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field
• Experience training machine learning models with PyTorch, onnxruntime, or Hugging Face Transformers
• Proficiency in Python and experience with ML libraries such as PyTorch, scikit-learn, and NumPy
• Understanding of training best practices including dataset management, batching, checkpointing, and loss functions
• Experience training in GPU/TPU environments and with distributed training frameworks (including PyTorch, onnxruntime)
• Experience training large-scale models (such as LLMs or multimodal models) and/or using cloud-based ML platforms
• Knowledge of MLOps practices including CI/CD, containerization, and model versioning
• Experience with performance profiling and memory optimization for training workflows
• Exposure to ethical AI practices, including fairness, explainability, and model auditing

Compensation and Benefits

• Base pay range: USD 129,300 to 193,900 per year
• Annual discretionary bonus program
• Opportunity for annual RSU grants
• Highly competitive benefits package