Software Engineer – Storage Middleware

Cornelis Networks delivers high-performance scale-out networking solutions for AI and HPC datacenters. They are seeking an experienced Senior Software Engineer to design, develop, and optimize middleware for storage applications, focusing on performance and reliability.

Responsibilities:

• Own the design and development of middleware for storage applications on CN platforms, with a focus on performance, scalability, and production readiness
• Work with customer-relevant storage technologies (Lustre, WEKA, BeeGFS, DAOS, etc.) to improve integration, resolve performance issues, and enable strong end-to-end behavior in distributed environments
• Analyze storage workloads, reproduce issues, develop benchmarks, and deliver fixes or enhancements based on real customer use cases and measured system behavior
• Collaborate across kernel/driver, firmware, fabric management and performance teams to support product development, ecosystem enablement, and advanced customer escalations
• Participate in design reviews, code reviews, CI, and long-term maintenance
• Contribute upstream open-source storage software projects where appropriate
• Operate as a self-motivated "owner," taking full responsibility for features from initial ideation through to remote deployment and maintenance
• Execute with precision in a fast-paced culture, balancing the need for rapid shipping with the long-term stability required for networking & storage infrastructure

Requirements:

• Experience in high-performance systems programming in C/C++ on Linux
• Experience developing, debugging, or optimizing storage middleware or related high-performance software
• Familiarity with storage technologies such as Lustre, WEKA, BeeGFS, DAOS, or similar distributed storage platforms
• Experience analyzing system stability and performance, identifying bottlenecks, and delivering production-quality fixes and improvements
• Strong collaboration skills and the ability to work across software, hardware, and customer-facing engineering teams
• Experience with distributed storage environments at cluster scale
• Experience contributing to open-source software or working in upstream-facing engineering environments
• Familiarity with performance testing, benchmarking, and tracing tools
• Experience supporting customer deployments or handling complex escalations
• AI-First Engineering: Demonstrate a 'proven AI-first' mindset by integrating advanced AI coding assistants and LLMs into the daily software development lifecycle to 10x output
• Technological Passion, Stay at the bleeding edge of AI research, proactively identifying opportunities to apply new LLM capabilities to networking hardware and software stacks