Staff Machine Learning Scientist

Staff Machine Learning Scientist

Requirements

• Deep understanding of deep learning principles and architectures (especially transformers).
• Extensive experience with multimodal machine learning concepts and techniques (for example, different fusion methods for text and images).
• Solid understanding of optimization techniques for large-scale models.
• Strong proficiency in Python and deep learning frameworks (PyTorch/TensorFlow) and model management libraries like HF Transformers.
• Experience with training large multimodal models with distributed training frameworks (for example, Horovod, MosaicML) and GPU fleet management.
• Strong understanding of knowledge representation concepts (for example, knowledge graphs, ontologies).
• Experience with distributed training frameworks and cloud computing platforms (for example, GCP, Azure).

Responsibilities

• Design and definition of the architecture of the LMMs, considering different fusion strategies and modality-specific processing.
• Implement, refine, benchmark and optimize model architectures using deep learning frameworks such as PyTorch or TensorFlow.
• Develop and manage the end-to-end training pipelines, including data loading, preprocessing, and model training. Architect and deploy distributed training workflows, optimizing for performance across cloud GPU fleets.
• Implement distributed training strategies to handle large-scale datasets and models.
• Design and implement methods to fuse knowledge with the multimodal representations within the LMM.
• Experiment with different approaches to enhance the model’s understanding and reasoning abilities through knowledge integration.
• Monitor and debug training processes, identifying and resolving performance bottlenecks.
• Collaborate with the knowledge integration engineer to ensure the architecture can accommodate knowledge injection mechanisms.

Preferred Qualifications

No preferred qualifications provided.