Sr. AI Engineer

Qualcomm•Santa Clara, CA

1d•$129,300 - $193,900

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.
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.
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.