Principal Software Engineer

Define technical direction for cloud infrastructure and machine learning platforms across multiple engineering teams. Design and review distributed systems that support model training, model inference, data processing, and platform services. Work with partner teams to align architecture, reliability, security, scalability, and operational requirements. Improve platform efficiency, including compute utilization, resource management, and service performance. Support Machine Learning Operations (MLOps) practices for model development, deployment, monitoring, and lifecycle management. Provide technical guidance for Kubernetes-based platforms and Artificial Intelligence (AI) workloads running in production environments. Contribute to long-term platform planning, technical standards, and engineering best practices across the organization. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, Go, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Master's Degree in Computer Science, Computer Engineering, or a related technical field AND 8+ years of technical engineering experience developing software in Go, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science, Computer Engineering, or a related technical field AND 12+ years of technical engineering experience developing software in Go, C, C++, C#, Java, JavaScript, or Python OR equivalent practical experience. Deep experience with Kubernetes, containers, cloud platforms, networking, storage systems, site reliability engineering, and large-scale distributed systems.

designing and operating machine learning platforms, large-scale model training environments, graphics processing unit infrastructure, distributed batch scheduling systems, machine learning operations frameworks, KubeRay, and Kueue.

Strong understanding of modern large language model and foundation model ecosystems, including training, inference, model serving, and observability.