Architect and build scalable, resilient, and high-performance backend infrastructure to support distributed training, inference, and data processing pipelines
Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale ML infrastructure
Design and implement core backend services (e.g., job schedulers, resource managers, autoscalers, model serving layers) with a focus on efficiency and low latency
Drive infrastructure optimization initiatives, including compute cost reduction, storage lifecycle management, and network performance tuning
Collaborate cross-functionally with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions
Continuously evaluate and integrate cloud-native and open-source technologies (e.g., Kubernetes, Kubeflow, MLFlow) to enhance our platform’s capabilities and reliability
Own end-to-end systems from design to deployment and observability, with a strong emphasis on reliability, fault tolerance, and operational excellence
Ensuring System Reliability: Ensure systems are designed and implemented with high availability, scalability, and performance. Focus on fault tolerance, disaster recovery, identifying and removing scaling bottlenecks, and performance optimization across our multi-cloud infrastructure
Observability & Monitoring: Develop, implement, and maintain comprehensive monitoring, alerting, logging, and tracing solutions to provide deep insights into system health and performance