Sr. AI Engineer
$129.3k - $193.9kQualcomm
Company: Qualcomm Technologies, Inc. Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: 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. Key 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.
- 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.
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