The Role: 

As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.

Key Responsibilities:

• Design and implement scalable infrastructure for large-scale model training workloads
• Develop and maintain distributed training pipelines for LLMs and multimodal models
• Optimize training performance across multiple GPUs, nodes, and data centers
• Implement monitoring, logging, and debugging tools for training operations
• Architect and maintain data storage solutions for large-scale training datasets
• Automate infrastructure provisioning, scaling, and orchestration for model training
• Collaborate with researchers to implement and optimize training methodologies
• Analyze and improve efficiency, scalability, and cost-effectiveness of training systems
• Troubleshoot complex performance issues in distributed training environments

Minimum Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
• 3+ years of experience with distributed systems and ML infrastructure
• Experience with PyTorch
• Proficiency in cloud platforms (AWS, GCP, Azure)
• Experience with containerization, orchestration (Kubernetes, Docker)
• Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)

Preferred Qualifications:

• Master's or PhD in Computer Science or related field
• Experience training large language models or multimodal AI systems
• Experience with ML workflow orchestration tools
• Background in optimizing high-performance distributed computing systems
• Familiarity with ML DevOps practices
• Contributions to open-source ML infrastructure or related projects