Deep Learning Model Optimization Engineer

About

Become a Deep Learning Model Optimization Engineer and elevate AI capabilities through advanced model training techniques. This role focuses on optimizing pre-training systems within a remote-capable, research-intensive environment.

You will be instrumental in next-generation AI model development, working hands-on with distributed GPU infrastructures. Your focus will be on designing and optimizing efficient training processes for large-scale models, ensuring maximum performance across thousands of NVIDIA GPUs. Collaborate with leading researchers and engineers to drive forward-thinking solutions in AI.

Key Responsibilities:

• Conduct large-scale pre-training for AI models
• Design and innovate model architectures for better performance
• Analyze and refine experimental methodologies
• Identify and solve performance bottlenecks
• Collaborate to build production-ready training systems

Requirements:

• PhD or equivalent in AI, ML, or related field
• Experience with large-scale LLM training on GPUs
• Deep understanding of transformer architectures
• Proficiency in debugging and optimizing AI systems
• Strong collaborative skills in research-focused teams

Successfully impact the AI landscape by optimizing training pipelines and enhancing model efficiencies in partnership with expert researchers.