Senior Forward Deployed Engineer

About SecurityScorecard:

SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard’s patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint.

Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace,” by Crain’s NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company’s annual list of the World’s Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing “forward-thinking employers for their unwavering commitment to employee engagement.” SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital.

About the Role:

SecurityScorecard is hiring a Staff Forward Deployed Engineer to embed with our most strategic customers and turn raw security data into decisions executives act on. You'll deploy onsite, learn the customer's workflow, data, and decision logic firsthand, and build SecurityScorecard directly into how they already operate, closing gaps and creating new value instead of bolting on a generic integration.

We're an AI product company at our core, so our FDEs build AI-first: using LLMs and modern AI tooling by default, and applying real data science to help customers quantify risk in terms finance already trusts.

This role follows a hybrid work model based on proximity to our office locations. Employees who live within 75 miles of our Midtown NYC or Downtown Austin office are required to work onsite 1–2 days per week. Employees who live outside a 75-mile radius of both offices are eligible for fully remote work.

What You'll Do

Deploy onsite with strategic customers to map their security, risk, and compliance workflows end to end, and find where value is leaking through manual effort or disconnected tools

Gather requirements from practitioners and executives, and understand the dependencies across their people, process, and data before proposing a solution

Weave SecurityScorecard into the customer's existing data, logic, and process, rather than forcing them to adapt to a generic integration

Build AI-native solutions by default: apply LLMs, ML models, and modern AI tooling to automate analysis and cut manual toil

Run data analysis and data science work on customer and platform data, for example translating letter-grade ratings into financial terms like Annualized Loss Expectancy (ALE), Value at Risk (VaR), and ROI

Design and deploy integrations, pipelines, and tools connecting SecurityScorecard to customer systems (SIEM, GRC, ticketing, data warehouses, and more)

Architect and deploy solutions across AWS, Azure, and GCP environments to match each customer's cloud footprint

Prototype rapidly in the field, then harden solutions into supportable, production quality software

Bridge Product, Engineering, and the customer, feeding field patterns back into the roadmap

Own technical relationships with key accounts through renewal and expansion, alongside Customer Success and Sales Engineering

Travel to customer sites for workshops, technical deep dives, and go-live support

Required Qualifications:

6+ years of engineering or technical deployment experience that includes customer-facing work.

Hands-on experience building with AI, using LLMs, agentic frameworks, or ML models as core building blocks in shipped solutions

A track record building AI-powered products or features, and a clear point of view on how AI changes forward deployed work

Working data analysis / data science ability: comfortable turning raw data into a model a customer can act on (risk quantification, benchmarking, exposure modeling)