Member of Technical Staff - Distributed Training Engineer

Liquid AI, spun out of MIT CSAIL, builds general-purpose AI systems that run efficiently across various deployment targets. The role focuses on designing, implementing, and optimizing distributed training infrastructure for large-scale training, requiring hands-on experience with distributed systems.

Responsibilities:

• Design and build core systems that make large training runs fast and reliable
• Build scalable distributed training infrastructure for GPU clusters
• Implement and tune parallelism/sharding strategies for evolving architectures
• Optimize distributed efficiency (topology-aware collectives, comm/compute overlap, straggler mitigation)
• Build data loading systems that eliminate I/O bottlenecks for multimodal datasets
• Develop checkpointing mechanisms balancing memory constraints with recovery needs
• Create monitoring, profiling, and debugging tools for training stability and performance