AI GPU Memory Optimization Engineer

About the role

Responsibilities

• Build GPU memory profiling tooling capturing fragmentation, allocation, and utilization patterns during training
• Identify and implement memory optimization strategies per model architecture and batch configuration
• Design automated configuration tuning that applies memory best practices across new workloads
• Evaluate trade-offs between gradient checkpointing, mixed precision, and optimizer state partitioning
• Create memory utilization benchmarks comparing configurations across A100 and H100 hardware
• Advise client ML teams on memory-efficient training architecture decisions

Requirements

• 6+ years in ML systems, GPU computing, or distributed training infrastructure
• Deep understanding of PyTorch memory management, gradient checkpointing, and optimizer state
• Experience with CUDA memory profiling tools and GPU hardware architecture
• Familiarity with DeepSpeed, FSDP, or similar distributed training frameworks
• Track record improving training efficiency through hardware-aware optimization

Benefits

• Fully remote with flexible schedule
• Top-of-market compensation for GPU systems expertise
• Comprehensive health benefits
• $5,000 annual equipment and conference budget
• Equity participation

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