Job Responsibilities: Identify and resolve infrastructure gaps to ensure reliable, efficient, and scalable solutions Develop advanced AI/ML infrastructure solutions that enhance the efficiency of our skilled ML teams Design and implement solutions for critical areas, including distributed storage systems, scheduling systems, high availability capabilities, and core reliability issues within our large-scale GPU clusters Monitor and optimize the performance of our AI/ML infrastructure, ensuring high availability, scalability, and efficient resource utilization Develop and deploy automation tools, monitoring solutions, and operational strategies to streamline infrastructure management and reduce manual tasks Work with various teams, including ML developers, data engineers, and DevOps professionals, to create a cohesive and integrated AI/ML infrastructure ecosystem

Minimum Skill Requirements: Bachelor's degree in Computer Science, Engineering, or related technical field 5-8+ years of experience in software engineering, with a strong background in developing and managing large-scale distributed systems, ideally within the AI/ML infrastructure domain Proficiency in programming languages such as Python, Go, or C++, with knowledge of cloud computing platforms like AWS, Azure, etc. Strong communication and collaboration abilities, effective in working with diverse teams and individuals Preferred Skill Requirements: In-depth understanding of AI/ML workflows, including model training, data processing, and inference pipelines Practical experience with containerization technologies (i.e., Docker, Kubernetes), automation tools (i.e., Ansible, Terraform), and monitoring solutions (i.e., Prometheus, Grafana) Exceptional problem-solving skills, capable of analyzing complex systems, identifying bottlenecks, and implementing scalable solutions A passion for continuous learning and staying abreast of new technologies and best practices in the AI/ML infrastructure space