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SecurityScorecard, the global leader in cybersecurity ratings with 12+ million companies continuously rated across 64 countries, is hiring a Senior Forward Deployed Engineer to embed with strategic customers and turn raw security data into actionable executive decisions.
In this role, you'll deploy onsite with key accounts to map their security, risk, and compliance workflows end-to-end, identifying where value leaks through manual effort or disconnected tools. You'll gather requirements from practitioners and executives, understand dependencies across people, process, and data, then weave SecurityScorecard into their existing systems rather than forcing generic integrations.
You'll build AI-native solutions by default, applying LLMs, ML models, and modern AI tooling to automate analysis and reduce manual toil. Your work includes running data analysis and data science on customer and platform data—translating letter-grade ratings into financial terms like Annualized Loss Expectancy (ALE), Value at Risk (VaR), and ROI. You'll design and deploy integrations, pipelines, and tools connecting SecurityScorecard to customer systems (SIEM, GRC, ticketing, data warehouses), architecting solutions across AWS, Azure, and GCP to match each customer's cloud footprint.
You'll prototype rapidly in the field, then harden solutions into production-quality software. You'll bridge Product, Engineering, and customers, feeding field patterns back into the roadmap. You'll own technical relationships with key accounts through renewal and expansion, working alongside Customer Success and Sales Engineering, and travel to customer sites for workshops, technical deep dives, and go-live support.
Required: 8+ years software engineering with significant customer-facing, forward-deployed, or solutions engineering experience. Hands-on AI experience building with LLMs, agentic frameworks, or ML models as core building blocks in shipped solutions. Track record building AI-powered products with clear perspective on how AI changes forward-deployed work. Working data analysis and data science ability—comfortable turning raw data into actionable models for risk quantification, benchmarking, and exposure modeling. Proven ability to ship production code independently in fast-moving, ambiguous, customer-facing environments. Strong full-stack fluency: backend services, APIs, scripting, data pipelines, integration patterns (REST, webhooks, SSO/SAML, SIEM connectors). Solid architecture design experience across AWS, Azure, GCP. Experience running discovery with enterprise stakeholders from practitioners to executives. Excellent communication translating technical detail to business/financial impact. Bias toward action and comfort with ambiguity. Prior cybersecurity, GRC, or risk management experience is a strong plus.
