Sr. AI Model Training Engineer

Qualcomm • Santa Clara, CA

58d

About The Position

We are looking for a skilled and motivated AI Model Training Engineer to join our team. In this role, you will be responsible for designing, training, fine-tuning, and optimizing machine learning models for a range of applications. You'll work closely with data scientists, researchers, and infrastructure engineers to develop robust, scalable models that meet performance, efficiency, and ethical standards.

Requirements

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
• OR
• Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
• OR
• PhD in Engineering, Information Systems, Computer Science, or related field.
• 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

Nice To Haves

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

Responsibilities

• Build and train machine learning and deep learning models using structured and unstructured datasets.
• Fine-tune pre-trained models (e.g., object detection, classification, LLMs, vision transformers) for specific downstream tasks.
• Design training pipelines with reproducibility, efficiency, and scalability in mind.
• Conduct hyperparameter optimization, model evaluation, and performance tuning.
• Collaborate with data engineering teams to ensure high-quality, well-labeled, and balanced datasets.
• Monitor training processes, identify failure modes, and address overfitting, underfitting, or bias.
• Keep up to date with the latest research and integrate state-of-the-art techniques into training workflows.
• Document models, training strategies, and experiments for internal knowledge sharing and compliance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Computer and Electronic Product Manufacturing
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees