Staff Software Engineer

Job description

Cohesity is the leader in AI-powered data security. Over 13,600 enterprise customers, including over 85 of the Fortune 100 and nearly 70% of the Global 500, rely on Cohesity to strengthen their resilience while providing Gen AI insights into their vast amounts of data. Formed from the combination of Cohesity with Veritas’ enterprise data protection business, the company’s solutions secure and protect data on-premises, in the cloud, and at the edge. Backed by NVIDIA, IBM, HPE, Cisco, AWS, Google Cloud, and others, Cohesity is headquartered in Santa Clara, CA, with offices around the globe.  We’ve been named a Leader by multiple analyst firms and have been globally recognized for Innovation, Product Strength, and Simplicity in Design , and our culture. Want to join the leader in AI-powered data security?  As an Performance Engineering Lead, you will architect and drive the development of generic, extensible infrastructure solutions for all datapath squads. Your primary focus will be to benchmark and analyze the performance of existing algorithms and systems, propose alternatives, and ensure the infrastructure is scalable, resilient, and adaptable to evolving needs.HOW YOU'LL SPEND YOUR TIME HERE: • Lead the design and implementation of extensible benchmarking infrastructure for distributed systems, supporting multiple protocols and storage backends. • Collaborate with datapath squads to identify performance bottlenecks, optimize system throughput, and evaluate alternative algorithms and architectures. • Develop tools and frameworks for automated performance testing, profiling, and regression analysis across thousands of nodes. • Ensure infrastructure supports hyper-convergence, distributed data paths, and web-scale architectures, including NAS, object storage, and cloud services. • Drive consensus protocols, asynchronous programming, and performance optimization initiatives. • Represent operational issues to senior leadership, providing deep technical insights and actionable recommendations. • Mentor and coach engineers, fostering a culture of quality, ownership, and continuous improvement.  WE'D LOVE TO TALK TO YOU IF YOU HAVE MANY OF THE FOLLOWING: • Minimum 10+ years of experience in infrastructure and back-end development, with a strong track record in large-scale distributed systems. • BS/MS/PhD in Computer Science or related field, with expertise in data structures, algorithms, and software design. • Expert-level programming and debugging skills in C, C++, Go. Expertise in — profiling, optimization, memory management, concurrency primitives (locks, atomics, thread pools, async I/O patterns) is an add-on • Deep understanding of system performance, scaling, multithreading, concurrency, and parallel processing. • Experience with NAS protocols (SMB, CIFS, NFS, S3), replication, disaster recovery, and distributed filesystems. • Proven ability to solve complex problems, debug production software, and drive tasks to completion. • Strong communication and analytical skills, with experience in technical feedback and code reviews. • Ability to build and lead successful technical teams, coaching and mentoring individuals. • Demonstrated experience leveraging AI tools to streamline workflows, enhance productivity, and support high-quality decision-making. Preferred Skills • Minimum 3+ years focused on performance engineering, systems optimization, or infrastructure for data-intensive systems. • Experience with RocksDB or similar LSM-tree based storage engines — tuning, benchmarking, write amplification analysis. • Familiarity with Protocol Buffers and RPC frameworks — understanding RPC latency profiling, serialization overhead, and async call flow optimization. • Experience with Prometheus, Grafana, or similar observability stacks for performance monitoring and dashboarding. • Background in file system internals (VFS, FUSE, NFS/SMB protocols) or block storage systems. • Experience with Google Test / Google Benchmark frameworks for C++ micro benchmarking. • Familiarity with containerized or clustered test environments (Ansible, Jenkins, distributed test orchestration). • Experience with Python for performance analysis tooling, data visualization, or automation scripts. • Background in network performance — TCP tuning, RDMA, kernel bypass techniques. • Publications, talks, or recognized contributions in the performance engineering community. • Proven track record of building benchmark infrastructure and regression detection systems at scale — including statistical methods for detecting regressions (t-tests, change-point detection, noise filtering). • Familiarity wi