Staff Software Engineer, Classification & Discovery

About Command|Link Command|Link is a global SaaS Platform providing network, voice services, and IT security solutions, helping corporations consolidate their core infrastructure into a single vendor and layering on a proprietary single pane of glass platform. Command|Link has revolutionized the IT industry by tackling the problems our competitors create. In recognition for our unprecedented innovation and dedication, Command|Link was recognized as the SD-WAN Product of the Year, ITSM Visionary Spotlight, UCaaS Product of the Year, NaaS Product of the Year, Supplier of the Year, and the AT&T Strategic Growth Partner. Command|Link has built the only IT platform for scale that solves ISP vendor sprawl and IT headaches. We make it easy for our customers to get more done, maximize uptime and improve the bottom line. Learn more about us here ! This is a 100% remote position About your new role: Discovery and automated topology are the single biggest unmet need in monitoring today, and the core of Meridian Softworks 's platform differentiation. This role owns the brain of the platform: the classification and discovery layer that turns raw telemetry into a living, queryable map of a customer's infrastructure. As data is ingested, you build the systems that determine what each host is, what it's doing, and how it connects to everything else. At the Staff level, that means more than building the systems well. It means owning the architectural model that underlies them, defining how topology evolves as new data sources are added, and ensuring the classification layer is a reliable foundation that adjacent teams in security, alerting, and observability can build against with confidence. Key Responsibilities: Own the architecture and long-term direction of the classification and discovery layer, including the topology model, entity resolution pipeline, and the graph data structures that represent customer infrastructure. Correlate diverse data sources, including security tooling, monitoring telemetry, syslog, OpenTelemetry, and L2-L4 network protocols, into accurate and queryable network and system topologies. Identify entity types (hosts, containers, users, services, network flows) from ingested telemetry and infer system and network topology from that signal. Own entity resolution and deduplication of discovered assets, including automated tagging and labeling of infrastructure at scale. Associate metric and log data with the correct entities and write enriched entity records into Memgraph. Build and maintain the layered topology model across IP, Interface, System, and Service layers, along with the dependency graph that relates devices and services to each other. Drive the Host Discovery and System Discovery experiences, including correlation confidence scoring and the evidence chains that let users approve, reject, or merge candidate systems. Lead the architectural shift toward streaming-first, event-driven topology materialization using Memgraph and Kafka, moving the platform away from periodic batch discovery. Set the technical standards and patterns that other teams depend on when consuming classification output, and drive alignment across those dependencies. Mentor engineers across the platform on data reasoning, graph modeling, and protocol-level thinking. Takes on additional responsibilities and projects as needed to support the success of the team and organization. What you'll need for success: Required: Strong Python background, including hands-on experience with Splink or comparable entity resolution frameworks. Production experience with Memgraph or another graph database, with solid graph traversal and modeling skills. Hands-on experience with Kafka and OpenSearch in streaming or event-driven architectures. Deep fluency with network and telemetry protocols at L2-L4, including syslog, SNMP, and OpenTelemetry, and the ability to reason about what real systems look like from the data they produce. Solid grasp of networking fundamentals: IP, interfaces, service detection, and how infrastructure components relate to each other. Experience with entity resolution, deduplication, and data-science approaches to classification problems. Experience with in-memory state management and dirty tracking in high-throughput systems. Demonstrated ability to own architectural decisions in ambiguous problem spaces and drive them to durable outcomes. Track record of cross-team technical influence, particularly in platform or data-layer roles where downstream teams depend on your contracts. Nice to Have: Experience providing structured context to LLMs for reasoning over topology, troubleshooting, or remediation workflows. Familiarity with osquery, Steampipe, or comparable endpoint and cloud inventory tooling. Background in network management platforms, NDR, or infrastructure observability products. Experience with stream processing frameworks such as Flink.How We Work We hand people real problems and expect them to run with them. The