Qualcomm Technologies, Inc. is looking for an AI Model Training Engineer to design, train, fine-tune, and optimize machine learning models for various applications, ensuring they meet performance, efficiency, and ethical standards.
Requirements
- 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
- 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.
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.
Other
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field.
- Background in performance profiling and memory optimization for training workflows.
- Exposure to ethical AI practices, including fairness, explainability, and model auditing.
