Technical Marketing Engineer
About the job
- A Bachelor's degree in Marketing, Business, Engineering, or a related field is required.
- At least 5 years of engineering experience is required.
- Proficiency with Cursor or similar artificial-intelligence-augmented integrated development environments for creating React, Vue, or Tailwind-based front-end demos.
- Familiarity with video capture and editing tools such as Camtasia, Loom, Descript, or Adobe Premiere.
- Working knowledge of BGP, gNMI, flow data, and OpenConfig.
- Strong analytical skills and the ability to use data to inform decisions.
- Ability to work independently in a fast-paced, dynamic environment.
- Develop technical assets including deep-dive white papers, reference architectures, technical blogs, and detailed how-to guides.
- Build, maintain, and script demo environments and provide sandboxes for the sales engineering team.
- Conduct hands-on laboratory testing of competing products and create technical competitive battlecards.
- Build and record technical tutorials to reduce time-to-value for new users.
- Use modern artificial intelligence coding tools such as Cursor, GitHub Copilot, or Replit to rapidly prototype custom front-end dashboards and visualization layers.
- Stay current on networking technologies, industry trends including AIOps, network automation, SASE, and SD-WAN, and competitor products.
- An MBA is preferred.
About the company
Selector AI provides an AI-powered observability and network intelligence platform that unifies monitoring and automates root cause analysis for large enterprises. It ingests, normalizes, and correlates metrics, logs, events, and configurations from hundreds of sources to deliver actionable insights through a single interface. Key components include Selector Copilot, a Network Language Model for natural language queries, and a Digital Twin to visualize dependencies and simulate changes, along with AI-driven root cause analysis. The goal is to reduce downtime and mean time to repair by speeding troubleshooting, advance AI capabilities, and help Fortune 1000 customers manage complex multi-cloud environments.
Company Size
51-200
Company Stage
Late Stage VC
Total Funding
$98M
Headquarters
Santa Clara, California
Founded
2019
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Simplify's Take
What believers are saying
- Selector raised $32 million in February 2026, lifting valuation to $375 million.
- Revenue quadrupled versus 2024, and 80% of customers are Fortune 1000.
- Gartner named Selector a 2026 Representative Vendor, strengthening enterprise credibility and pipeline.
What critics are saying
- Datadog, Dynatrace, and Splunk compress Selector’s observability wedge by 2027.
- Agentic NetOps increases false-remediation and outage liability if Foundry misfires in production.
- A handful of Fortune 1000 telecom and enterprise logos create concentration risk and slow expansion.
What makes Selector Software unique
- Selector Foundry runs agents where telemetry already lives, avoiding external data copying.
- Eight U.S. patents in January 2026 defend causal inference, path intelligence, and LLM training.
- Selector correlates network, cloud, and application signals into one causal root-cause view.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Unlimited Paid Time Off
Paid Holidays
Paid Vacation
Paid Sick Leave
Parental Leave
Performance Bonus
Growth & Insights and Company News
6 month growth
2%1 year growth
2%2 year growth
0%Selector has raised $33 million in Series B funding led by Ansa Capital, bringing total funding to over $66 million. The AI-driven network observability company also launched Selector Foundry, a platform enabling network operations teams to build and manage their own AI agents. The funding round included participation from AT&T Ventures, Bell Ventures, Singtel Innov8, and Hyperlink Ventures, alongside existing investors Two Bear Capital, Atlantic Bridge, and Sinewave Ventures. Selector Foundry allows operators to create agents within the platform where telemetry data is collected, eliminating the need to copy operational data to external services. Agents are stored in Git repositories and validated against historical incidents before deployment. The company will use the funds to expand its AIOps and large language model technologies, and establish new offices across the US, Canada, Europe, Singapore, India, and Japan.
Selector Foundry brings AI agents to network operations, enabling organizations to build, version, and run on their own data. Selector introduced Selector Foundry, a development and runtime environment that lets network operations teams build, test, version and govern their own AI agents inside the Selector platform. Agents execute where telemetry is already collected, so no operational data is copied to an external agent service. Selector's platform resolves signals from network, cloud, infrastructure and application sources into a single causal picture with a named probable cause rather than a ranked list of possibilities. Until now, the work performed by operators following that correlation has remained manual: assembling evidence, opening and chasing the ticket, pulling the right teams onto a bridge and deciding on remediation, with each step increasing time to resolution. Selector Foundry agents take on that sequence, and because they consume Selector's correlated output rather than raw logs, they begin from evidence instead of re-deriving it. Each response returned by the orchestrator agent contains a recommended action and the evidence behind it; operators still decide whether to act on it, so response moves faster without the risks associated with full automation. No two networks are the same, and Selector's position is that this makes operator authorship a requirement rather than a feature. Two enterprises running identical hardware still differ in topology, vendor mix, naming conventions, maintenance windows, escalation paths and what each team considers normal. A link that flaps nightly is an incident in one environment and expected behavior in another. A vendor-authored agent has no way to know the difference, because that knowledge exists in the operations team's runbooks and in the judgment of the engineers who have run the network for years. Selector Foundry makes that knowledge executable: the people who already know which conditions matter are the people who write the agents that act on them. With Selector Foundry, the senior operator is the author, and the output is treated as software. Agents are defined, committed to the customer's own Git repository, and reviewed through the team's existing pull-request workflow. Before promotion, an agent is replayed against the customer's own historical event data, producing a pass/fail comparison against the recorded outcome of each past incident. A failed promotion is rolled back in a single step. Agents trigger on events, on a schedule, through a REST API call, or on operator request, and execute under the RBAC and identity controls Selector already enforces. Selector Foundry ships with six generally available agents, so teams are not starting from an empty runtime. The included agents investigate incidents from correlated evidence, open and update records in IT Service Management (ITSM) systems with root-cause context, generate decision-ready views and stakeholder reports, and operate across cloud estates, covering asset health, connectivity and spend. All six agents arrive in the same repository as customer-authored agents, and a team can run them unmodified or fork them to match its own runbooks. "Other organizations are building agent platforms and asking customers to send their network data to it. We put the agents where the network already is, so your team builds them the way any other software gets built: they design it, review it, test it, and promote it when it's ready. They are yours, in your repository, on your data," said Nitin Kumar, co-founder and CEO of Selector. David Marshall is the founder of VMblog.com, one of the industry's longest-running independent publications covering modern data center technologies. What began as a focus on virtualization and cloud computing has expanded to cover the full spectrum of enterprise IT, including AI, security, and DevOps, making VMblog a trusted destination for vendor news, technology analysis, and industry commentary.Beyond publishing, David has spent his career at the intersection of technology and business, inventing, marketing, and launching a number of successful software companies and products, and building a reputation as a skilled marketing executive in the enterprise IT space.David is also a published author, having written two well-regarded books on virtualization and served as technical editor for two "For Dummies" titles covering virtualization and cloud computing. He co-founded CloudCow.com, a publication focused on cloud computing, and has been named a VMware vExpert every year since 2009, one of the longest continuous honoree streaks in the program's history.Connect with David on LinkedIn: https://www.linkedin.com/in/davidmarshall/
Selector has launched an AI-powered multi-cloud observability solution to address visibility gaps in hybrid network environments. The platform correlates signals across cloud, network and infrastructure to accelerate root cause analysis. The solution unifies telemetry data from public cloud, private cloud and on-premises data centres into a shared intelligence layer. Selector's patented data ingestion model harmonises data whilst preserving context across domains, enabling teams to identify issues and their impact across the full hybrid path. Key capabilities include unified multi-cloud data ingestion, cloud-change awareness, capacity intelligence, end-to-end path visualisation and cross-domain correlated alerts. The solution is now available across leading cloud platforms, allowing existing customers to extend visibility without disrupting workflows. Selector's platform aims to help organisations reduce mean time to resolution and prevent outages in increasingly complex hybrid environments.
Selector launches ai-powered multi-cloud observability solution, closing the network-to-cloud visibility gap. Selector announced the expansion of its platform with AI-powered multi-cloud observability capabilities. The extension of Selector's AI-driven observability approach into multi-cloud environments enables organizations to correlate signals across the full hybrid path. By unifying rich telemetry data from cloud, network, and infrastructure into a shared intelligence layer, Selector gives teams a more complete, actionable view of incidents and true root cause. As cloud adoption and hybrid infrastructure expansion have become the operational norm, teams need a way to preserve context across the full environment rather than investigating network, infrastructure, and cloud issues in separate silos. While the hybrid path has become mission-critical, correlation across that path was never built into traditional monitoring architecture. The resulting complexity and dependencies on domain-specific tools make finding root causes harder and outages last longer, thereby increasing operational burden on enterprises. For enterprises migrating to hybrid environments, this visibility gap is no longer just inconvenient; it is costly. Unlike tools that treat cloud and network observability as separate domains, the Selector solution is built around the end-to-end operational path. Its differentiation starts with how data is ingested, enriched, and correlated from the point of collection onward. Selector's patented data ingestion model harmonizes the data across different domains while preserving context across cloud, network, and infrastructure telemetry. Selector AI and ML engines work on this harmonized data to correlate disparate signals from across domains, identify what changed, determine where an issue started, and explain how far the impact extends. The new capabilities include: * Unified Multi-Cloud Data Ingestion: Selector ingests and harmonizes data from public cloud, private cloud, and on-prem data center environments, providing unified visibility across cloud and hybrid deployments. * Cloud-Change Awareness: Teams can detect infrastructure and configuration changes across cloud environments, helping identify issues such as routing changes or misconfigurations before they cascade into broader incidents. * Cloud Usage and Capacity Intelligence: Users gain deep visibility into utilization across cloud constructs and connectivity paths to support right-sizing, planning, and more efficient cloud operations. * Vendor-Agnostic Data Pipeline: Organizations can now unify on-prem and cloud signals without forcing teams into separate monitoring silos or requiring replacement of existing tooling. * End-to-End Path Visualization and Validation: Organizations can now visualize the full path from on-prem to cloud networks and validate reachability, latency, and connectivity before issues impact users. * Cross-Domain Correlated Alerts and Root Cause Analysis: Users can apply the same correlation and analytics across network and cloud telemetry to reduce noise, and accelerate root cause analysis. * Topology-Aware Context: Selector preserves the relationships between assets, services, dependencies, and network paths, maintaining the operational context needed to understand incidents across hybrid environments. "Modern infrastructure is hybrid by default, but most operations workflows remain fragmented," said Nitin Kumar, CTO at Selector. "Selector's solution brings cloud into the same operational model as network observability, giving teams one correlated view across the hybrid path, so they can see the full context, reduce noise, and get to the true root cause faster. Selector's AI-powered multi-cloud observability solution is available immediately across leading cloud platforms. Existing customers can extend visibility into multi-cloud and hybrid environments without disrupting established workflows. David Marshall has been involved in the technology industry for over 30 years, and he's been working with virtualization software since 1999. He became a pioneer in the virtualization and cloud computing field - one of the few people in the industry allowed to work with Alpha stage server virtualization software from industry leaders: VMware (ESX Server), Connectix and Microsoft (Virtual Server).Through the years, he has invented, marketed and helped launch a number of successful software companies and products. David holds a BS degree in Finance, an Information Technology Certification, and a number of vendor certifications. He's also co-authored two published books: "VMware ESX Essentials in the Virtual Data Center" and "Advanced Server Virtualization: VMware and Microsoft Platforms in the Virtual Data Center" and was the technical editor for two popular Virtualization "For Dummies" books. With his remaining spare time, David founded and operates one of the oldest independent modern data center publications, VMblog.com. And co-founded CloudCow.com, a publication dedicated to Cloud Computing. Since 2009, and each year thereafter, David has been honored with the vExpert distinction by VMware by Broadcom for his evangelism.Connect on LinkedIn: https://www.linkedin.com/in/davidmarshall/
Selector, a Santa Clara-based AI-driven network intelligence startup, has raised $32 million in funding led by AVP, doubling its valuation to $375 million. Ansa Capital, Two Bear Capital, Sinewave Ventures and Singtel Innov8 participated in the round. The company reported cumulative annual recurring revenue reached 230% of 2024 levels, marking its fourth consecutive year of doubling ARR. New ARR bookings climbed to 370% of 2024 levels. Selector added three Fortune 20 customers and now counts approximately 80% of its customer base from Fortune 1000 organisations. Selector's platform correlates data across network, cloud and application domains to identify incident root causes. The company recently secured eight US patents and plans to release Agentic ChatOps capabilities for multi-turn reasoning across complex infrastructure environments.