Member of Technical Staff - Machine Learning Engineer, Training Infrastructure

You're A Great Fit If

• You have extensive experience building distributed training infrastructure for language and multimodal models, with hands-on expertise in frameworks like PyTorch Distributed, DeepSpeed, or Megatron-LM
• You're passionate about solving complex systems challenges in large-scale model training—from efficient multimodal data loading to sophisticated sharding strategies to robust checkpointing mechanisms
• You have a deep understanding of hardware accelerators  and networking topologies, with the ability to optimize communication patterns for different parallelism strategies
• You're skilled at identifying and resolving performance bottlenecks in training pipelines, whether they occur in data loading, computation, or communication between nodes
• You have experience working with diverse data types (text, images, video, audio) and can build data pipelines that handle heterogeneous inputs efficiently

What Sets You Apart

• You've implemented custom sharding techniques (tensor/pipeline/data parallelism) to scale training across distributed GPU clusters of varying sizes
• You have experience optimizing data pipelines for multimodal datasets with sophisticated preprocessing requirements
• You've built fault-tolerant checkpointing systems that can handle complex model states while minimizing training interruptions
• You've contributed to open-source training infrastructure projects or frameworks
• You've designed training infrastructure that works efficiently for both parameter-efficient specialized models and massive multimodal systems

What You'll Actually Do

• Design and implement high-performance, scalable training infrastructure that efficiently utilizes our GPU clusters for both specialized and large-scale multimodal models
• Build robust data loading systems that eliminate I/O bottlenecks and enable training on diverse multimodal datasets
• Develop sophisticated checkpointing mechanisms that balance memory constraints with recovery needs across different model scales
• Optimize communication patterns between nodes to minimize the overhead of distributed training for long-running experiments
• Collaborate with ML engineers to implement new model architectures and training algorithms at scale
• Create monitoring and debugging tools to ensure training stability and resource efficiency across our infrastructure

What You'll Gain

• The opportunity to solve some of the hardest systems challenges in AI, working at the intersection of distributed systems and cutting-edge multimodal machine learning
• Experience building infrastructure that powers the next generation of foundation models across the full spectrum of model scales
• The satisfaction of seeing your work directly enable breakthroughs in model capabilities and performance