Security Research Engineer, Attack Surface & Risk Signals

Cybercube-Analytics; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

CyberCube Analytics, Inc. in San Francisco is seeking a Quantitative Cyber Risk Engineer to bridge network engineering, cybersecurity, and data science. You will design signals measuring exposure and work with product, analytics, and engineering teams to integrate risk signals into models.

You will translate complex internet configurations into actionable metrics and help shape analytics for the cyber insurance industry.

Qualifications

• Deep understanding of how the internet works at a foundational level (TCP/IP, BGP, DNS, HTTP/S, TLS).
• Ability to apply mathematical/statistical concepts to real-world risk problems.
• Experience translating complex network topologies into measurable data points.
• Educational background in a highly quantitative/technical field.
• 3–5+ years in network engineering, cyber threat research, risk modeling, or related domain.

Responsibilities

• Design innovative, quantitative signals that measure exposure to cyber attacks based on network architecture and internet footprint.
• Translate theoretical cybersecurity concepts into empirical, actionable risk metrics and assumptions.
• Architectural deep-dives to extract insights from internet-facing protocols, cloud infrastructure, and enterprise networks.
• Conduct research on emerging cyber threats and map them to asset configurations.
• Collaborate with product, analytics, and engineering to integrate risk signals into production models.

Skills

Internet Fundamentals

Quantitative Modeling

Networking Protocols

Problem Solving

Education

Bachelor's or Master's in CS/Math

Tools

Python

SQL

Spark/Hadoop

Job description

About CyberCube:

CyberCube delivers the world's leading analytics to quantify digital risk, helping the largest global carriers, reinsurers and brokers understand, price and manage cyber risk in financial terms.

• AI at our foundation, cyber risk at our core. We don't just use AI, we shape it.
• Built on AI from day one. Artificial intelligence has been part of our strategy since the beginning, blended with deep cybersecurity and insurance expertise and backed by rigorous testing.
• Trusted by more than 100 clients, including 75% of the top 40 European and US cyber insurance carriers and 70% of the top ten reinsurance brokers.
• Backed for global growth. In 2025 Spectrum Equity joined some of CyberCube’s financial partners as a new cornerstone investor, accelerating our global growth and fueling innovation across our end-to-end cyber risk analytics for the insurance industry.
• A truly global team across San Francisco, New York, London, and Tallinn.
• collaboration, openness, intellectual rigor, and ownership for excellence.
• People at the forefront. We encourage CyberCubers to challenge themselves, push boundaries, and do the best work of their careers.

Quantitative Cyber Risk Engineer

About the Role Are you fascinated by the underlying architecture of the internet? Do you look at complex network configurations and immediately see the quantitative risk exposure?

As a Quantitative Cyber Risk Engineer, you will play a critical role at the intersection of network engineering, cybersecurity, and data science. We are looking for an expert who deeply understands how the internet is wired—down to the protocol level—and possesses the quantitative prowess to translate that knowledge into computable risk metrics.

In this role, you will analyze how global businesses configure their internet-facing assets and design mathematical signals that accurately quantify their exposure to cyber attacks. Working alongside Data Scientists, Engineers, and Product teams, your research and models will directly drive the next generation of risk-scoring and analytical engines for the cyber insurance industry.

If you are passionate about turning theoretical network vulnerabilities into actionable, real‑world risk signals that influence a global industry, this is the role for you.

Key Responsibilities

• Design Risk Signals: Develop innovative, quantitative signals that accurately measure a company’s exposure to cyber attacks based on their network architecture, asset configurations, and internet footprint.
• Quantify the Unquantifiable: Translate theoretical cybersecurity concepts and complex network topologies into empirical, actionable risk metrics and assumptions.
• Architectural Deep-Dives: Evaluate and extract meaningful insights from how organizations deploy and configure internet-facing protocols, cloud infrastructure, and enterprise networks.
• Threat Landscape Research: Conduct in-depth research on emerging cyber threats (e.g., critical vulnerabilities, ransomware, trending exploits, data breach techniques) and map them to underlying asset configurations.
• Cross-Functional Collaboration: Serve as the subject matter expert on internet architecture, working closely with product, analytics, and engineering teams to integrate your risk signals into production models.

Must-Have Skills & Qualifications

• Deep Internet & Networking Expertise: Expert-level understanding of how the internet works at a foundational level. You should be intimately familiar with network protocols (TCP/IP, BGP, DNS, HTTP/S, TLS), routing, ASN ecosystems, and complex enterprise network configurations.
• Strong Quantitative Acumen: Proven ability to apply mathematical and statistical concepts to real-world problems. You must be able to design quantitative metrics and build logic that scores and sizes risk exposure accurately.
• Analytical Mindset: Exceptional problem-solving skills with a track record of conceptualizing complex, abstract security concepts and breaking them down into measurable data points.
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related highly quantitative/technical field.
• Experience: 3-5+ years of relevant work experience in network engineering, cyber threat research, risk modeling, or a related domain.

Good-to-Have (Preferred) Skills

• Coding for Signal Creation: Proficiency in Python or R to prototype algorithms and turn your theoretical risk concepts into actual programmatic outputs.
• Data Querying: Experience with SQL and relational databases to pull, manipulate, and analyze large datasets of asset and configuration data.
• Big Data Exposure: Familiarity with big data frameworks (Spark / Hadoop), flat files, complex data types (JSON, XML), and working with APIs.
• Industry Context: Experience working in or adjacent to the cyber insurance, risk modeling, or data science industries.
• Certifications: Advanced technical or security certifications (e.g., CISSP, CISM, or advanced Cisco/networking certifications) are a plus, but hands‑on expertise matters most.

Who might thrive here
This role is a fit for people from many backgrounds. You might be a Security Researcher, Threat Researcher, Penetration Tester or Offensive Security Engineer who knows how attackers find and exploit exposed assets. Or you might come from network engineering, detection engineering, or attack surface management and know exactly how internet-facing infrastructure gets set up and misconfigured. If you've done bug bounty or red team work, or built security data and risk models, and you want to turn that experience into signals that shape a global industry, we'd love to hear from you.

AI is reshaping how work gets done across every function. We value people who are curious about AI, eager to learn, and thoughtful about applying AI tools to work more effectively. AI fluency is part of how we assess every role in our hiring process.

Research shows the best candidates rarely match a job description point for point. If you're excited about this role and believe you could make an impact, we'd love to hear from you, even if your experience doesn't line up perfectly with everything listed above.

CyberCube Analytics, Inc. and CyberCube Analytics Europe Limited is an equal opportunity employer. We don’t tolerate discrimination against age, gender, gender identity, gender expression, sexual orientation, race, color, nationality, ethnicity, religion, disability, veteran status, protected genetic information or political affiliation.